RAG Systems: Building AI That Doesn't Hallucinate

Retrieval-Augmented Generation (RAG) is the technique that makes AI systems accurate and reliable by grounding their responses in your actual data.

The Hallucination Problem

Large Language Models (LLMs) are trained on internet data with a knowledge cutoff. When asked about:

• Your specific products or services
• Internal company policies
• Recent events after training
• Specialized domain knowledge

They either make things up (hallucinate) or admit they don't know.

RAG solves this by retrieving relevant information from your data before generating a response.

How RAG Works

The Basic Flow

User Query
    ↓
Query Processing (reformulation, expansion)
    ↓
Vector Search (find relevant documents)
    ↓
Context Assembly (combine top results)
    ↓
LLM Generation (answer using context)
    ↓
Response Validation
    ↓
User Response

Key Components

1. Document Processing

• Split documents into chunks (typically 500-1000 tokens)
• Clean and normalize text
• Extract metadata (dates, authors, categories)

2. Embedding Generation

• Convert text chunks to vector embeddings
• Popular models: OpenAI text-embedding-3-large, Cohere embed-v3
• Embeddings capture semantic meaning

3. Vector Database

• Store embeddings for fast similarity search
• Options: Pinecone, Weaviate, Qdrant, Chroma, pgvector

4. Retrieval

• Convert user query to embedding
• Find most similar document chunks
• Return top-k results (typically 3-10)

5. Generation

• Inject retrieved context into LLM prompt
• Generate response based on provided information
• Cite sources when possible

Building a Production RAG System

Step 1: Data Preparation

Chunking strategies:

• Fixed size: Simple but may cut mid-sentence
• Semantic: Split on paragraph/section boundaries
• Recursive: Hierarchical splitting with overlap
• Document-aware: Respect structure (headers, lists)

Best practice: Include 10-20% overlap between chunks to maintain context.

Step 2: Choose Your Embedding Model

Step 3: Vector Database Selection

Managed services:

• Pinecone: Easiest to use, managed infrastructure
• Weaviate Cloud: Hybrid search, good filters
• Qdrant Cloud: Performance-focused, affordable

Self-hosted:

• pgvector: PostgreSQL extension, familiar tooling
• Chroma: Simple, good for prototypes
• Milvus: Enterprise-scale, complex queries

Step 4: Retrieval Optimization

Hybrid search: Combine vector similarity with keyword matching (BM25)

Query transformation:

• Query expansion (add synonyms)
• Hypothetical document embeddings (HyDE)
• Multi-query generation

Re-ranking: Use cross-encoder models to re-score results

Step 5: Prompt Engineering

Effective RAG prompt structure:

System: You are a helpful assistant. Answer questions based
only on the provided context. If the context doesn't contain
the answer, say "I don't have information about that."

Context:
{retrieved_documents}

User question: {query}

Answer:

Advanced RAG Techniques

Multi-Vector Retrieval

Generate multiple representations per document:

• Summary embedding
• Question embeddings (what questions does this answer?)
• Keyword embeddings

Parent Document Retrieval

Store small chunks for retrieval, return larger parent documents for context.

Self-Querying

Let the LLM generate metadata filters:

Query: "What were our Q3 2024 sales?"
Generated filter: date >= 2024-07-01 AND date <= 2024-09-30 AND type = "sales_report"

Query Routing

Route queries to specialized indexes:

• Product queries → Product database
• Support queries → Knowledge base
• Policy queries → HR documents

Corrective RAG (CRAG)

Evaluate retrieved documents for relevance. If confidence is low:

1. Try web search
2. Decompose query
3. Ask for clarification

Evaluation Metrics

Retrieval Quality

Generation Quality

Tools for Evaluation

• RAGAS: RAG Assessment framework
• LangSmith: LangChain's evaluation platform
• Arize Phoenix: Open-source observability
• Custom: Build evaluation datasets

Common Pitfalls

1. Poor Chunking

Problem: Chunks that split important information Solution: Test different chunking strategies, use overlap

2. Irrelevant Retrieval

Problem: Vector search returns wrong documents Solution: Hybrid search, better embeddings, metadata filtering

3. Lost in the Middle

Problem: LLMs focus on start/end of context, missing middle Solution: Limit context length, re-rank by importance

4. Context Overflow

Problem: Too much context exceeds token limits Solution: Better retrieval, summarization, chunking

5. No Citation

Problem: Users can't verify information Solution: Include source references in responses

Real-World Applications

Customer Support Bot

• Index: Help articles, FAQs, product docs
• Result: 85% reduction in support tickets
• Key: Include recent update handling

Legal Document Assistant

• Index: Contracts, regulations, case law
• Result: 10x faster research
• Key: Precise citation requirements

Enterprise Knowledge Base

• Index: Confluence, SharePoint, internal wikis
• Result: 40% faster information finding
• Key: Access control integration

Medical Information System

• Index: Clinical guidelines, research papers
• Result: Consistent, evidence-based responses
• Key: Strict hallucination prevention

Getting Started

Minimum Viable RAG

1. Data: Collect your documents
2. Embeddings: OpenAI or open-source
3. Vector DB: Start with Chroma or pgvector
4. LLM: GPT-5.3 Codex or Claude Opus 4.6 for generation
5. Interface: Simple chat UI

Scaling Considerations

• Document updates: Real-time vs. batch indexing
• Multi-tenancy: Separate indexes per customer
• Cost optimization: Caching, smaller models
• Latency: Pre-computation, edge deployment

AWZ Digital builds custom RAG systems for enterprise knowledge management. Contact us to discuss your use case.

Author: David Chen, AI Engineer Published: January 2026