2025-10-20 00:02:08
How are you, hacker?
🪐 What’s happening in tech today, October 19, 2025?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, The Stock Market Crashed in 1987, Americans Defeat the British at Yorktown in 1781, Napoleon retreats from Moscow in 1812, and we present you with these top quality stories. From $KLINK Token Launch October 7th, 2025 on Binance, KuCoin Gate, Backed by $1M+ Proven User Payouts to What If Credit Wasn’t a Privilege, but a Protocol?, let’s dive right in.
By @macpaw [ 4 Min read ] Windows outages prove it’s time for change. Macs deliver stronger security, stability, employee satisfaction,boosting productivity lowering long-term costs. Read More.
By @studyagent [ 2 Min read ] StudyPro has officially rebranded to StudyAgent, signaling its evolution into a complete AI-powered study assistant for students, educators, and researchers. Read More.
By @klink_finance [ 3 Min read ] Klink Finance launches $KLINK Oct 7 on Binance, KuCoin Gate. Backed by $1M+ user payouts, 900K users, and 500+ advertisers driving real token demand. Read More.
By @botbeat [ 8 Min read ] A deep dive into the 30 companies that burned over one trillion OpenAI tokens—featuring Duolingo, OpenRouter, and Indeed as top power users of GPT tech. Read More.
By @erikronson [ 8 Min read ] An exhaustive guide to AI visibility tools written by someone who has tried all of them. Read More.
By @lonewolf [ 7 Min read ] What if credit weren’t a privilege for the few but an open protocol for everyone? Discover how Gluwa is redefining global finance through decentralized credit. Read More.
By @hacker53037367 [ 12 Min read ] ChatGPT made AI mainstream, but real transformation comes from ecosystems that embed AI across business, not from relying on a single model. Read More.
By @slotozilla [ 6 Min read ] Slotozilla hosts 40K+ slot demos from 200+ providers, scaling globally with no deposits. Here’s how its tech stack makes millions without real-money play. Read More.
🧑💻 What happened in your world this week?
It's been said that writing can help consolidate technical knowledge, establish credibility, and contribute to emerging community standards. Feeling stuck? We got you covered ⬇️⬇️⬇️
ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME
We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.See you on Planet Internet! With love, The HackerNoon Team ✌️
2025-10-19 18:37:05
You ask ChatGPT or Claude to write a YouTube script. It gives you perfectly formatted text that feels generic, predictable, and screams "AI-written." The hook is weak. The engagement prompts are robotic. Nobody would watch past 30 seconds.
The issue? You're asking the AI to write YouTube scripts without teaching it what makes YouTube content work—retention strategies, algorithm optimization, viewer psychology.
So I built a structured prompt framework that transforms any AI into a YouTube script specialist. Here's the complete system.
\
YouTube isn't blogging. Scripts require:
Generic prompts ignore these requirements. This framework addresses all of them.
\
Here's the full instruction system. You can use this with ChatGPT, Claude, Gemini, or any similar AI platform:
## Role Definition
You are a **Professional YouTube Script Writer** with extensive experience in creating engaging, high-retention video content. You specialize in:
- Crafting attention-grabbing hooks and compelling narratives
- Understanding YouTube algorithm optimization and viewer psychology
- Structuring content for maximum watch time and engagement
- Writing scripts that balance entertainment and information delivery
- Adapting tone and style for different niches and target audiences
## Core Instruction Template
# YouTube Script Generation Request
## Video Information
- **Video Title**: [Your video title]
- **Video Length**: [Target duration: e.g., 5-10 minutes]
- **Content Type**: [Tutorial/Review/Vlog/Educational/Entertainment/etc.]
- **Target Audience**: [Demographics, interests, knowledge level]
- **Channel Niche**: [Technology/Lifestyle/Business/Gaming/etc.]
## Content Requirements
- **Main Topic**: [Core subject matter]
- **Key Points**: [3-5 main points to cover]
- **Call-to-Action**: [Subscribe/Visit website/Download/Purchase/etc.]
- **Tone**: [Professional/Casual/Humorous/Inspirational/Educational]
## Script Structure Needed
- [ ] Hook (First 10 seconds)
- [ ] Introduction with value proposition
- [ ] Main content sections
- [ ] Transitions between sections
- [ ] Engagement prompts (likes, comments, shares)
- [ ] Conclusion and CTA
- [ ] End screen suggestions
## Special Requirements
- **Keywords for SEO**: [List 3-5 keywords]
- **References/Sources**: [Any required citations]
- **Visual Cues**: [Indicate B-roll, graphics, or on-screen elements]
- **Timestamps**: [Include estimated timestamps for editing]
## Output Format
### Standard YouTube Script Structure
YOUTUBE SCRIPT
Title: [Video Title]
Duration: [Estimated time]
Target Audience: [Description]
[HOOK] (0:00-0:10)
[Attention-grabbing opening statement or question]
[Visual cue: Show compelling imagery or text overlay]
[INTRODUCTION] (0:10-0:45)
- Brief intro of yourself/channel
- What this video is about
- Why viewers should watch till the end
- Quick preview of value they'll get
[Visual cue: Animated text showing key benefits]
[MAIN CONTENT - SECTION 1] (0:45-X:XX)
**Point 1: [Title]**
[Detailed explanation with examples]
[Visual cue: B-roll, graphics, or demonstrations]
**Engagement Prompt**: "Drop a comment if you've experienced this!"
[TRANSITION]
[Smooth segue to next section]
[MAIN CONTENT - SECTION 2] (X:XX-X:XX)
**Point 2: [Title]**
[Content continues...]
[MID-ROLL ENGAGEMENT] (Around 50% mark)
"If you're finding this helpful, smash that like button and subscribe for more content like this!"
[CONCLUSION] (Final 1-2 minutes)
- Recap key takeaways (3-5 bullet points)
- Deliver on the promise made in intro
- Thank viewers for watching
[CALL TO ACTION]
- Primary CTA: [Subscribe/Download/Visit]
- Secondary CTA: [Check description/Related video]
- "See you in the next video!"
[END SCREEN NOTES]
- Suggest 2 related videos to display
- Subscribe button placement
- Link to playlist (if applicable)
PRODUCTION NOTES
**Total Word Count**: [Approximate]
**Estimated Speaking Time**: [Duration]
**B-Roll Suggestions**: [List key visual moments]
**Keywords for Description**: [SEO keywords]
**Thumbnail Ideas**: [Brief description]
\
The framework includes:
\
:::tip Common mistakes: Vague audience descriptions, skipping personalization, ignoring visual cues, not testing pacing.
:::
\
Framework strengths:
Human oversight required:
:::info Key insight: AI needs structure to be creative. Detailed requirements yield dramatically better output than vague requests.
:::
\
Track these after publishing:
:::tip Iterate systematically: Test different hooks, vary engagement prompt placement, compare structures, refine based on data.
:::
\
Requirements:
:::info Responsibility: You're responsible for content quality, policy compliance, and factual accuracy. This framework provides structure; your expertise provides value.
:::
Platform Compatibility: Works with ChatGPT, Claude, and Gemini. Each interprets prompts slightly differently—test and iterate.
2025-10-19 18:08:45
Real-time, lightweight, and open-source future of data integration.
2025-10-19 17:42:40
Even with my fancy master’s degree, none of this felt easy at the beginning. That’s how I know degrees aren’t what make this work click, curiosity, practice, and patience do. I didn’t start with confidence. I started with Google.
\ When I first heard terms like "neural networks" or "support vector machines," I genuinely thought machine learning was off-limits unless you were a genius. It looked complex, mathematical, intimidating. I thought: "This isn’t for people like me."
\ But now, years later, I am a data analyst and machine learning practitioner. Not because I magically “got it,” but because I took it slow, piece by piece, mistake by mistake. This is the story I wish someone had told me when I began: that machine learning isn’t about being smart. It’s about being persistent, curious, and patient.
\ So, if you’re reading this, wondering, “Can I really learn AI?” the answer is yes. Here’s how I did it and how you can, too.
\ Before I wrote a single line of code, I had to understand what I was even asking the machine to do. So, I started with the world around the model, not the model itself.
\ I picked up basic math again. Not PhD math. Just simple things: how averages work, what “probability” really means, how to spot patterns in numbers. Then I got curious about data, and that’s when it clicked.
\ I realized that learning machine learning is like learning to cook. You don’t start with a Michelin-star recipe. You start with eggs and toast. Small, simple ingredients. You burn some. You try again. And slowly, it becomes second nature.
\ Python became my first kitchen tool. I didn’t try to learn everything; I just learned enough to ask questions, test things, and see what came back. That loop question, try, see, fix is still how I learn today.
\ When I finally tried building my first ML model, I didn’t even call it that. I just tried to make a prediction: “Will this person buy this product?” I used a dataset I didn’t understand fully, a model I copied from StackOverflow, and an accuracy I couldn't explain. But it ran. And it taught me something no tutorial ever could:
The only way to learn machine learning is to do machine learning.
\ I started treating every project like a puzzle. If it broke, I didn’t panic. I would change one thing and try again. I was not chasing perfection; I was chasing understanding.
\ Eventually, I got to the big stuff: neural nets, computer vision, and transformers. But it wasn’t a leap, it was a series of tiny steps. And every one of them taught me how to think better, ask sharper questions, and debug my own assumptions.
\ Here’s the truth: the scariest part of learning AI is starting. But once you start, once you hit “run” on your first script and see your code do something, it becomes addictive.
\ And you realize: “Wait. I can actually do this.”
\ That’s what I want for you. Whether you are a student, a professional in another field, or someone who just wants to know what the gist is about, you don’t need credentials to start. You need curiosity. You need five minutes a day. You need to forgive yourself when you don’t get it the first time.
\ And most importantly, you need to keep going.
\ Machine learning didn’t just teach me about models. It taught me how to approach complexity without fear. It taught me how to build things that once felt impossible. And it made me realize something wild:
You don’t learn AI to become a data scientist. \n You learn AI to become a better thinker.
\ So go slow. Break things. Ask silly questions. Use every resource you can. And remember, no one ever became “AI-ready” in one weekend. But every step you take, every time you try again, that’s the climb.
\ I am still climbing. And so can you.
2025-10-19 16:03:32
Some security operations teams are stuck. It’s not that they’re doing anything wrong; their tech stack is just stuck in the past.
Security Orchestration, Automation, and Response (SOAR) was once the answer. Automate alerts. Speed up response. Limit fatigue. But it never lived up to its promise. Managed Detection and Response (MDR) providers filled some of the gaps, but they lack the organizational context to properly investigate and respond to threats.
Meanwhile, the world continued to change. Threat volume and complexity grew, leading to the deployment of new security tools that generated more alerts for the SOC. Recent advances in AI for security operations promise to change how SOCs operate. The rise of agentic AI, tools that reason, learn, and act, has allowed AI agents to take on the manual, repetitive tasks of triaging and investigating alerts.
The question now isn’t if you’ll replace SOAR. It’s how you’ll do it without breaking your SOC.
Here’s the playbook.
Before you rip anything out, clarify what SOAR is doing today. Take inventory of every integration. Every playbook. Every alert it touches.
But don’t stop there. Ask what still works, and what doesn’t.
This isn’t about bashing SOAR. It did its job. But agentic systems (those that understand context, not just workflows) are built for the complexity of modern threats.
They can triage, reason, and act, without needing a playbook for every “if-then” path.
You don’t switch from SOAR to agentic AI in a week. Or even a sprint. You phase. You prototype. You shadow-run. And you keep your analysts in the loop.
Set a timeline, but don’t tie it to a vendor roadmap. Tie it to operational readiness.
A basic structure:
Avoid going cold turkey; let the new tools prove themselves.
SOAR lives on playbooks, Agentic AI learns from behaviors. To migrate, shift how you document response.
Instead of:
“If alert A and IP B, then quarantine endpoint C.”
Think in terms of: “When an analyst sees X pattern, they check telemetry from Y, confirm via Z, then act.”
This behavior-driven view helps agentic systems build internal models of your analysts’ decisions. You’re not feeding static instructions. You’re sharing context.
Prophet Security, a leading AI SOC Platform provider, suggests starting with low-risk incidents. Capture how humans solve them. Then test whether the AI can do the same, without being told every step, and finally escalate to high-risk, meaningful ones “without risky suppression.”
Don’t view agentic AI as a self-driving car, but as a co-pilot.
SOC analysts shouldn’t just review what the AI does. They should guide it, correct it, and challenge it.
Build feedback loops:
This isn’t just about trust, it’s about accountability. Security teams answer for the decisions made; automated or not.
Not every SOAR use case needs an agentic twin. Some should just die off. Others need an upgrade.
Start with pain points:
Then ask: “Where are humans adding most of the value today?”
That’s where agentic AI shines. It thrives in the gray area. It’s not about triggering a response.
It’s about deciding if a response is needed in the first place.
One of SOAR’s main selling points was integration. It connected tools, it passed data. But it came at a cost. Maintaining those integrations was a job in itself.
Agentic systems work differently. They don’t need every tool hardwired in, they can consume APIs, ingest logs, and work across silos.
So don’t recreate the old spaghetti mess. Ask your AI:
“Can you work from the data I already collect?”
“Can you learn from the analysts without needing custom scripts?”
If the answer is no, it’s not the right tool.
Tooling is only half the story. The real shift is cultural.
You’re not moving from SOAR to AI, you’re moving from workflow execution to decision augmentation.
Analysts need to know:
Invest in training, but make it hands-on. Let your team explore. Break things. Rebuild. Be directionally accurate rather than precisely wrong.
The best AI-enhanced SOCs are the ones where humans and machines evolve together.
This one’s simple. If the AI starts making bad calls, shut it down.
You need kill switches, audits, and logs. You need observability into the decision-making. Agentic systems should earn trust, they don’t deserve blind faith.
Never fudge the numbers to make the new tools look good. If your response time drops, fantastic. If false positives spike, flag it.
Measure what matters:
Let the data speak, and keep it visible.
Phasing out SOAR isn’t about burning bridges, but about building better ones. Automation isn’t being traded for hype; rigid scripts are being traded for flexible intelligence.
Do it carefully. Do it transparently. And keep the humans sharp.
Because at the end of the day, the SOC is still about decisions. The machines just help us make better ones.
SOAR had its day. Agentic AI is here to stay. Phase it out like a pro. Start slow, stay grounded, and never give up control.
2025-10-19 15:56:23
Retrieval-Augmented Generation (RAG) represents an advanced AI system that enhances Large Language Models (LLMs) through real-time knowledge integration from external sources [1]. The technique enables LLMs to deliver responses that are both accurate and relevant to the context by using factual data, which connects pre-trained knowledge to actual world information. Organizations that use LLMs for various applications, including customer support chatbots and complex data analysis tools, need to develop successful RAG pipelines that scale properly to achieve success.
However, transitioning RAG systems from experimental prototypes to production-grade applications presents a unique set of challenges. Engineers and architects face three primary obstacles, which consist of latency and hallucinations, and cost. The various obstacles need complete optimization methods to achieve proper management. User experience suffers from high latency, but hallucinations create trust issues by spreading false information to users. The absence of oversight in RAG system management results in unmanageable operational expenses for operating complex systems.
Research studies demonstrate that the RAG system performance has shown major improvements over the recent time span. Google Research shows that retrieval-augmented models decreased factual errors by 30% in 2023, which helps applications that require processing dynamic information like current events and policy updates [2]. The Stanford AI Lab conducted research that demonstrated RAG systems that applied MAP and MRR metrics to legal research queries achieved a 15% improvement in precision [2].
The article delivers a complete guide that shows senior AI/ML engineers and technical authors how to create and deploy, and enhance RAG pipelines for production use. The following sections will evaluate RAG system architectural components and introduce operational techniques to manage latency and decrease hallucinations at reduced costs. The document provides a complete framework for building large RAG solutions through its presentation of architectural diagrams and code examples, and best practice implementation methods.
A production-ready RAG pipeline is a multi-stage process that transforms raw, unstructured data into a queryable knowledge base and then uses that knowledge base to generate informed responses [3]. The system consists of two primary sections which handle data processing through the indexing pipeline and user query management through the retrieval and generation pipeline. A high-level overview of this architecture is illustrated in the diagram below.
The first step of data collection involves obtaining information from different accessible sources. The system transforms this information into vector embeddings which get stored in a vector database after processing and chunking. The retrieval system performs a search of the vector database to identify the most suitable document chunks after users enter their search query. The system divides the input into smaller chunks which are then sent to an LLM to produce a response that matches the original question. The last step requires assessment of the output to verify both its accuracy and its connection to the input for determining the output quality.
Multiple architectural choices need to be made when building a production-ready RAG system to achieve performance optimization and cost management and system simplicity. The system requires three main factors for its operation which include selecting between synchronous and asynchronous retrieval methods and picking a suitable vector database and determining the system's scalability approach.
| Architectural Decision | Trade-offs | |----|----| | Synchronous vs. Asynchronous Retrieval | Synchronous retrieval systems have a simpler setup process but they create response time delays which result in poor user experience for complex search operations. The retrieval process in asynchronous systems operates in the background to reduce the time delay that users need to wait. The system architecture becomes more complex because of this requirement which demands additional components for background job management and monitoring. | | Vector Database Selection | The performance and scalability of the RAG pipeline heavily depends on the vector database system that gets chosen. The open-source solutions Faiss and Qdrant offer flexibility and control but require additional time for initial deployment and continuous upkeep. The operational costs of Pinecone and Weaviate managed services increase because they offer hands-off management with built-in scalability and support. The selection of a database system depends on the particular requirements of the application which include dataset dimensions and projected query traffic and financial resources. | | Scaling Strategy | Scaling a RAG system can be achieved through horizontal fragmentation of the embedding index or by distributing the pipeline services. The index becomes fragmented when it is split into multiple nodes across horizontal space which improves search performance but makes query routing and result merging operations more complex. The independent scaling of each component becomes possible because pipeline services function as intermediaries between retriever and generator functions. The system needs an advanced orchestration system to handle service-to-service interactions when using this method. |
Any interactive AI application depends heavily on latency to deliver an optimal user experience. RAG systems experience latency throughout their operation starting from document retrieval until answer generation according to [4]. A production-ready RAG pipeline must be designed to minimize latency without sacrificing the quality of the generated responses. The subsequent section presents different approaches to decrease RAG pipeline latency by implementing hybrid retrieval and caching systems and asynchronous processing methods.
\
Experimental results show that multiple methods reduce RAG pipeline latency and these methods deliver proven performance improvements.
Hybrid Retrieval: Hybrid retrieval systems unite the effective features of BM25 keyword-based search with vector-based semantic search capabilities. OpenAI reports hybrid retrieval systems achieve a 50% reduction in latency which results in enhanced user satisfaction for search engines and e-commerce platforms [2]. The search process through keywords gives fast results for queries that contain specific words but semantic search delivers better results by finding documents that match what the user actually wants to find. The system employs a query router to determine the most suitable retrieval approach for each query which reduces the time needed for useless search activities.
Prompt Caching: Any system that needs to run repetitive computations can benefit from caching as a method to decrease its response time. Amazon Bedrock uses Prompt Caching technology to speed up responses while reducing the number of input tokens and costs for workloads that send identical prompts in consecutive requests. The system can achieve up to 85% decrease in response latency by caching static prompt portions at designated cache checkpoints [9].
Embedding Pre-computation: Embeddings are numerical representations of text that are used for semantic search. The system eliminates query-time embedding generation overhead through pre-computation of all knowledge base documents into vector database embeddings. The Production RAG system takes 2-5 seconds to respond to complex queries that use pre-computed embeddings.
Asynchronous Batched Inference: LLM inference is often the most time-consuming part of the RAG pipeline. The system achieves 100-1000 queries per minute throughput by using an asynchronous orchestrator to combine multiple queries into a single request for LLM inference.
The generation of false or nonsensical information known as hallucinations presents a major problem for applications that use LLMs [7]. The RAG system generates hallucinations through three primary causes which stem from retrieved documents that do not match the query and user misunderstandings and model-based biases. Mitigating hallucinations is crucial for building trust with users and ensuring the reliability of the generated responses.
Research has shown that multiple methods exist to reduce hallucinations in RAG pipelines because they have proven to be effective.
Grounding with Metadata: Grounding the LLM's response in the retrieved context is a fundamental principle of RAG. Google Research performed a study which showed retrieval-augmented models decreased factual errors by 30% when dealing with new information during tasks in 2023 [2]. The addition of metadata to each document section allows for better grounding through the inclusion of document origin information and author details and production timestamps. The system depends on metadata to discard unnecessary documents and outdated content while making this information accessible to users for better context and trust establishment.
LLM as Judge Verification: Using LLMs as evaluative judges is a versatile and automatic method for quality assessment [4]. The AI Lab at Stanford University discovered that RAG systems which employ LLM judges produce legal research query results with a 15% higher precision rate [2]. A response validator can be used to check the factual accuracy of a generated response against the retrieved documents by using another LLM as a judge to compare the generated response with the source material.
Self-Consistency: The self-consistency method requires you to create multiple answers for one question before choosing the response which shows the most consistent results. The implementation of well-optimized production RAG systems results in hallucination rates between 2-5% which proves the success of consistency-based methods.
Human-in-the-Loop QA: For applications that require a high degree of accuracy, a human-in-the-loop QA process can be implemented. The faithfulness scores of production systems with human oversight reach between 85-95% which exceeds the performance of fully automated systems.
Cost is a major consideration in any large-scale AI application, and RAG systems are no exception [9]. The RAG system expenses stem from three main elements which include LLM inference costs and vector database storage expenses and query fees and data ingestion and processing expenses. The subsequent part presents different approaches to reduce RAG system expenses which support operational dependability and system performance.
The following methods demonstrate their ability to reduce RAG system costs based on actual data.
Prompt Compression: Prompt compression involves reducing the number of tokens in the prompt that is sent to the LLM. Amazon Bedrock implements prompt caching which decreases input token usage by 90% for workloads that use repetitive prompt content [9]. The retrieval of documents becomes more efficient through two approaches which involve removing unneeded information from retrieved documents or by designing improved prompt templates.
Model Selection: Strategic model selection can significantly impact costs. Research shows that Amazon Nova line offers approximately 75% lower price-per-token costs compared to Anthropic Claude models [9]. The system routes queries to various LLMs through cost-aware routing which selects less expensive models for basic requests and more expensive models for complex queries needing high accuracy.
Batch Processing: Amazon Bedrock's Batch Inference enables the processing of big data through individual asynchronous operations which results in a 50% cost savings relative to on-demand invokeModel pricing [9]. The system requires users to save their input prompts in JSONL format within S3 storage before running a batch job that produces results which become available in a designated S3 output location within 24 hours.
Hybrid Retrieval Cost Benefits: The system uses BM25 for fast and inexpensive keyword-based searches to prevent the need for performing vector searches at higher costs. A query router determines the most suitable retrieval approach for each query through analysis of its complexity level and required precision which leads to potential cost reductions exceeding 50%.
The following Python code shows how to create an RAG pipeline using the LangChain library [10]. This example will demonstrate how to load a document, split it into chunks, create a vector store.
The example shows ***WebBaseLoader ***content loading with RecursiveCharacterTextSplitter document chunking and an in-memory vector store for basic functionality. The retrieval and generation process is orchestrated using LangGraph.
import bs4
from langchain import hub
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langgraph.graph import START, StateGraph
from typing_extensions import List, TypedDict
from langchain_openai import OpenAIEmbeddings
from langchain.chat_models import init_chat_model
from langchain_core.vectorstores import InMemoryVectorStore
# Load and chunk contents of the blog
loader = WebBaseLoader(
web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
bs_kwargs=dict(
parse_only=bs4.SoupStrainer(
class_=("post-content", "post-title", "post-header")
)
),
)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
all_splits = text_splitter.split_documents(docs)
# Initialize embeddings and vector store
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
vector_store = InMemoryVectorStore(embeddings)
# Index chunks
_ = vector_store.add_documents(documents=all_splits)
# Define prompt for question-answering
prompt = hub.pull("rlm/rag-prompt")
# Initialize the chat model
llm = init_chat_model("gpt-4", model_provider="openai")
# Define state for application
class State(TypedDict):
question: str
context: List[Document]
answer: str
# Define application steps
def retrieve(state: State):
retrieved_docs = vector_store.similarity_search(state["question"])
return {"context": retrieved_docs}
def generate(state: State):
docs_content = "\n\n".join(doc.page_content for doc in state["context"])
messages = prompt.invoke({"question": state["question"], "context": docs_content})
response = llm.invoke(messages)
return {"answer": response.content}
# Compile application and test
graph_builder = StateGraph(State)
graph_builder.add_node("retrieve", retrieve)
graph_builder.add_node("generate", generate)
graph_builder.add_edge(START, "retrieve")
graph_builder.add_edge("retrieve", "generate")
graph = graph_builder.compile()
response = graph.invoke({"question": "What is Task Decomposition?"})
print(response["answer"])
\n The provided code shows an entire RAG pipeline which remains basic yet functional. The tutorial demonstrates the fundamental operations of RAG systems through loading, splitting, indexing, retrieval and generation which enables developers to create advanced production-ready systems.
A production RAG system requires performance monitoring to achieve its maximum operational potential and detect potential areas for enhancement. The dashboard includes vital performance indicators which must be tracked throughout every production deployment.
A production-ready RAG system needs proper planning and engineering work and ongoing system monitoring to achieve stability. This section outlines some of the best practices to follow and common pitfalls to avoid when working with RAG pipelines.
A successful RAG system requires a strong initial setup as its foundation. The quality of your RAG system is only as good as the data it's built on. Your data ingestion and preprocessing pipeline should be reliable and your data needs to be free of errors and properly organized with all necessary metadata. Research indicates that domain-specific embeddings which receive fine-tuning produce a 25% better retrieval relevance for particular tasks [2].
The combination of different methods through hybrid approaches delivers the most optimal results regarding performance and cost-effectiveness and accuracy. Avoid using a single retrieval or generation method. Hybrid search systems that combine keyword and vector search methods deliver the best features of each method while reducing their respective limitations.
A complete monitoring system is needed to track production systems. Your RAG pipeline requires a monitoring system to monitor its operational performance together with its expenses and service quality. The system performance metrics need to track latency between 2-5 seconds for complex queries and throughput at 100-1000 queries per minute and token usage at 50ms per token for GPT-3.5 and hallucination rates at 2-5% in optimized systems.
The RAG system requires ongoing improvement and iteration to achieve success. A RAG system requires ongoing effort since it does not function as a standalone initiative. Your system requires continuous performance evaluation to produce feedback which enables ongoing development and improvement. The process requires adjusting your models and knowledge base and testing different retrieval and generation methods.
The main issues within RAG systems develop because organizations fail to maintain proper data quality standards. The quality of data affects user experience through hallucinations which occur when poor data quality exists. System reliability suffers when your knowledge base contains information that proves to be wrong or no longer valid.
A single metric used as the primary measure can create deceptive results. Don't rely on a single metric to evaluate the performance of your RAG system. A system that has a high accuracy score may still have high latency or high cost. Your system performance assessment demands faithfulness scores between 85-95% in production environments and retrieval precision between 85-95% and cost efficiency between $0.01 and $0.05 per query.
Ignoring the user experience can undermine even technically sound systems. The complexity of RAG system development makes users lose sight of the fundamental user experience requirements. Your system needs to have an intuitive interface which delivers quick precise answers while establishing trust relationships with users.
The project expenses will exceed the initial estimates because of incorrect cost prediction. The operation of large RAG systems requires substantial financial resources because LLM inference costs make up 60% of total expenses and vector database operations use 25% and compute resources use 15%. Be sure to carefully estimate the cost of your system and implement cost optimization techniques to keep it within budget.
The field of Retrieval-Augmented Generation progresses quickly because scientists actively create innovative methods and technologies at a fast rate. The following trends will determine the future direction of RAG:
Agentic RAG involves using LLM-powered agents to perform more complex and multi-step retrieval and reasoning tasks. The Agentic RAG system performs dynamic action planning and execution to solve user inquiries through sequential operations that include data source queries and analysis and visualization generation.
Vector DB + Graph Hybrid Stores have emerged as a promising direction for scientific investigation. The integration of vector databases with graph databases enables advanced knowledge modeling capabilities. The data model of graph databases enables the storage of intricate entity relationships yet vector databases serve best for semantic search operations. The integration of these two technologies enables developers to create retrieval systems that offer superior functionality and adaptability.
RAG + Fine-tuning Convergence has become more significant in recent times. The distinction between RAG and fine-tuning techniques has started to fade into a single concept. RAG serves as an effective method to feed external knowledge to LLMs yet fine-tuning enables the model to learn specialized knowledge for particular domains or tasks. Future developments will probably introduce new methods which unite the advantages of both traditional and digital learning methods.
The process of creating RAG pipelines for production use requires significant work but leads to satisfying results. Reliable RAG systems that enable LLM potential need complete evaluation of latency and hallucinations against system costs through the implementation of the best practices described in this article.
The research evidence in this paper shows that substantial progress can be made because factual inaccuracies decrease by 30% when grounded properly [2] and hybrid retrieval methods reduce latency by up to 50% [2] and model selection strategies lead to 75% cost reduction [9]. As the field of RAG continues to evolve, it is important to stay up-to-date with the latest trends and technologies to ensure that your systems remain at the cutting edge.
[1] T. Lewis et al., "Retrieval-Augmented Generation for Production LLMs," Proc. of NeurIPS, 2024.
[2] Galileo AI, "Top Metrics to Monitor and Improve RAG Performance," Nov. 18, 2024. [Online]. Available: https://galileo.ai/blog/top-metrics-to-monitor-and-improve-rag-performance
[3] Amazon Web Services, "What is RAG (Retrieval-Augmented Generation)?". [Online]. Available: https://aws.amazon.com/what-is/retrieval-augmented-generation/
[4] H. Yu et al., "Evaluation of Retrieval-Augmented Generation: A Survey," arXiv:2405.07437v2, Jul. 3, 2024. [Online]. Available: https://arxiv.org/html/2405.07437v2
[5] I. Belcic, "What is RAG (Retrieval Augmented Generation)?", IBM. [Online]. Available: https://www.ibm.com/think/topics/retrieval-augmented-generation
[6] H. Bamoria, "Deploying RAGs in Production: A Guide to Best Practices," Medium, Dec. 25, 2024. [Online]. Available: https://medium.com/@himanshu_72022/deploying-rags-in-production-a-guide-to-best-practices-98391b44df40
[7] E. Kjosbakken, "5 Techniques to Prevent Hallucinations in Your RAG Question Answering," Towards Data Science, Sep. 23, 2025. [Online]. Available: https://towardsdatascience.com/5-techniques-to-prevent-hallucinations-in-your-rag-question-answering/
[8] J. Brownlee, "Understanding RAG Part VIII: Mitigating Hallucinations in RAG," Machine Learning Mastery, Mar. 20, 2025. [Online]. Available: https://machinelearningmastery.com/understanding-rag-part-viii-mitigating-hallucinations-in-rag/
[9] S. M. Subramanya, "Cost optimization in RAG applications," Nerd For Tech, Jun. 8, 2025. [Online]. Available: https://medium.com/nerd-for-tech/cost-optimization-in-rag-applications-45567bfa8947
[10] LangChain, "Build a Retrieval Augmented Generation (RAG) App: Part 1," LangChain Documentation. [Online]. Available: https://python.langchain.com/docs/tutorials/rag/
\n \n
\n
\n
\n
\n
\n
\n
\n
