🎯 By the end of this module, you will:
Retrieval-Augmented Generation (RAG)
RAG bridges the gap between frozen model weights and dynamic, private enterprise documents without costly fine-tuning.
Raw docs are segmented into 256-512 token chunks with 10-20% overlap so crucial conditions are never sliced in half.
# 1. Semantic Chunking with Overlap
from langchain_text_splitters import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=400,
chunk_overlap=60,
separators=["\n\n", "\n", ". ", " "]
)
chunks = splitter.split_text(raw_enterprise_policy)Mental Model: Closed-Book Cramming vs. The Open-Book Exam
• Closed-Book Exam (Standalone LLM): A student tries to memorize 10,000 pages of corporate bylaws. When asked about a specific 2026 refund formula, their memory blurs and they invent a convincing fake answer (Hallucination).
• Open-Book Exam with a Library (RAG): The student doesn't memorize every nuance. When asked the question, they walk to the catalog, pull the exact SLA handbook, and quote the exact sentence with page citations.
RAG as a Workflow Node vs. Autonomous Agent Tool
Compare fixed pipeline execution (where retrieval is mandatory on every step) against agentic tool calling (where the agent autonomously decides if, when, and what to query).
Interactive Architectural Paradigm: RAG as a Node vs. RAG as a Tool
Compare deterministic pipeline retrieval (Node) against dynamic agentic self-directed search (Tool).
Vector Knowledge Base Retrieval
Query vector DB ➔ Ingest Top-3 policy chunks
When to use RAG as a Node:
• Mandatory Domain Pipelines: Customer support chatbots where every single inquiry MUST check the official FAQ before replying.
• Predictable SLAs & Cost: Exactly one retrieval vector search per request. Zero risk of the agent looping or skipping search.
RAG merely shifts the problem from "making things up from weights" to "retrieval quality and context adherence". If your vector database returns irrelevant or truncated chunks, the model will hallucinate around the noise!
RAG Lifecycle & Noise Impact Studio
Experiment with document chunk size in the Ingestion phase, and observe how noisy vector retrieval triggers hallucinations in the Inference phase.
RAG Lifecycle & Retrieval Quality Laboratory
Test the Ingestion Phase (Chunking & Embedding) and the Inference Phase (Vector Search & Noise Impact).
Raw unstructured documents must be segmented into chunks before they can be converted into numerical vectors.
"Tier 1 enterprise clients receive 99.99% uptime guarantees. Scheduled maintenance occurs on the first Sunday..."
"...unplanned outage exceeding 15 minutes, enterprise accounts are eligible for a 15% billing credit upon formal claim..."
# Agentic RAG: Giving the Agent a Knowledge Search Tool
from langchain_core.tools import tool
@tool
def search_corporate_kb(query: str, domain: str = "all") -> str:
"""Search internal enterprise knowledge base for policies, SLAs, and technical manuals."""
# 1. Embed incoming search query
query_vector = embed_model.embed_query(query)
# 2. Query Vector DB with metadata filtering
results = vector_db.query(
vector=query_vector,
filter={"domain": domain} if domain != "all" else None,
top_k=2
)
# 3. Format grounded passages with citation IDs
return "\n".join([f"[{r.id}] {r.metadata['title']}: {r.text}" for r in results])
# Agent can now autonomously decide IF and WHEN to call search_corporate_kb!
agent = initialize_agent(tools=[search_corporate_kb, calculate_refund])Architectural Traps in RAG Systems
RAG does NOT eliminate hallucinations; it shifts the problem to retrieval quality. If your vector database returns irrelevant chunks, the model will hallucinate around the noise to please the prompt!
Splitting text strictly by token count without sentence overlap risks slicing critical conditions in half (e.g. "Except in cases of..." moves to chunk 2, inverting the legal meaning in chunk 1). Always include 10-20% chunk overlap!
Key Architectural Takeaways
- 1.Decouple Ingestion from Inference: Ingestion (chunking, embedding, indexing) happens offline; Inference (vector similarity, grounding) happens live.
- 2.Use RAG as a Tool for Dynamic Agents: Give the agent an explicit search tool so it can query multiple databases conditionally rather than forcing rigid pipeline retrievals.
- 3.Enforce Strict Relevance Filters: Discard retrieved chunks with low cosine similarity scores (<0.80) to prevent the model from hallucinating plausible nonsense.
Evaluating AI Agent Frameworks
Compare LangGraph, CrewAI, AutoGen, LlamaIndex, and the OpenAI Agents SDK to choose the exact right framework for your production architecture.