Knowledge Retrieval (RAG)
Retrieval-Augmented Generation (RAG) connects an LLM to your data. Since LLMs have a cutoff date and don't know your private documents, RAG solves this by first *searching* for relevant information in a database and then *pasting* it into the prompt context. This grounds the answer in facts and reduces hallucinations.
When to Use
- Domain-Specific QA: Answering questions about internal documentation, legal contracts, or medical records.
- Dynamic Data: When the information changes frequently (news, stock analysis).
- Verifiability: When the answer must cite sources ("According to policy document A...").
- Cost: To avoid fine-tuning models on new data, which is expensive and slow.
Use Cases
- Enterprise Search: "How do I reset my VPN password?" (Searches IT Wiki).
- Legal Analysis: "Summarize the liability clause in this contract." (Searches contract PDF).
- Customer Support: "What is my order status?" (Searches Order Database).
Implementation Pattern
def rag_workflow(user_query):
# Step 1: Retrieval
# Convert query to vector and search vector DB
relevant_docs = vector_db.similarity_search(user_query, k=3)
# Step 2: Prompt Construction
# Combine the context with the user question
context_text = "\n".join([doc.content for doc in relevant_docs])
prompt = f"""
You are a helpful assistant. Answer the question based ONLY on the context below.
Context:
{context_text}
Question: {user_query}
"""
# Step 3: Generation
answer = llm.generate(prompt)
return answer