Built With

AI Agents Built With RAG (Retrieval-Augmented Generation)

RAG is what makes AI agents actually useful for your business instead of just generically smart. It connects the LLM to your specific data — products, policies, processes, customer history — so the agent gives answers grounded in reality, not hallucinated from training data. Every knowledge-intensive agent needs RAG.

Support agents with RAG resolve 75-85% of knowledge-based questions accurately versus near-zero without it. One client's internal knowledge agent cut information search time from 22 minutes average to 15 seconds.

The Technology

Why I Use RAG (Retrieval-Augmented Generation)

Without RAG, your AI agent knows everything GPT-4 learned during training and nothing about your business. It can't answer 'what's our refund policy for enterprise clients?' or 'what did we decide in the Q3 product planning meeting?' because that information doesn't exist in the model's training data. RAG fixes this by giving the agent access to your actual documents, databases, and knowledge bases at query time.

The technique is straightforward: your documents are split into chunks, converted into numerical embeddings, and stored in a vector database (Supabase pgvector, Pinecone, Weaviate). When a user asks a question, the query is also converted to an embedding, the most similar document chunks are retrieved, and those chunks are included in the LLM's prompt as context. The model answers based on your data, not its training data.

RAG isn't magic — it has failure modes. Retrieval can miss relevant chunks if your chunking strategy is wrong. The model can still hallucinate despite having correct context. Large documents may need hierarchical retrieval strategies. But a well-built RAG system gives you 85-95% accuracy on questions about your own data, which is dramatically better than the 0% accuracy you get without it. I build RAG into every knowledge-intensive agent I deploy.

Capabilities

What RAG (Retrieval-Augmented Generation) Enables

Grounded responses referencing specific documents and sources for verifiability

Real-time knowledge updates without model retraining or fine-tuning

Hybrid search combining semantic similarity with keyword matching

Configurable retrieval: top-k, MMR, contextual compression, re-ranking

Multi-source retrieval across documents, databases, APIs, and web content

Chunk management and metadata filtering for precise knowledge access

In Practice

How I Use RAG (Retrieval-Augmented Generation) in Agent Systems

A RAG-powered agent retrieves relevant information from your knowledge base before generating each response. When a customer asks about your return policy, the agent searches your policy documents, retrieves the relevant section, and answers with a citation. The response is grounded in your actual policy — not a guess based on general knowledge about return policies.

Use Cases

RAG (Retrieval-Augmented Generation) in Action

Customer support agents answering from product docs and help articles

Legal agents searching case law, contracts, and regulatory databases

Internal knowledge assistants finding information across wikis, docs, and systems

Sales enablement agents referencing pricing, specs, and competitive intel

Onboarding agents guiding new employees through company policies and procedures

FAQ

RAG (Retrieval-Augmented Generation) Questions

How accurate is RAG compared to fine-tuning?

RAG is better for factual accuracy on your specific data. Fine-tuning teaches the model patterns and style, but it doesn't reliably memorize specific facts. RAG retrieves the actual source document at query time, so the answer is grounded in current data. Use RAG for factual knowledge. Use fine-tuning for tone, style, and specialized reasoning patterns.

What's the best vector database for RAG?

For most deployments: Supabase with pgvector. It's PostgreSQL-native, scales well, and you're probably already using Supabase for other things. For high-volume or specialized needs: Pinecone (managed, fast), Weaviate (hybrid search), or Qdrant (performance-focused). The database matters less than your chunking strategy and retrieval logic.

How do I keep RAG knowledge current?

Set up automated re-indexing. When a Google Doc is updated, a Notion page changes, or a new help article is published, a sync job re-chunks and re-embeds the updated content. Frequency depends on how fast your knowledge changes — hourly for support docs, daily for most business content, weekly for policies.

Why does my RAG agent sometimes give wrong answers?

Three common causes: chunks too large (retrieves irrelevant context alongside relevant content), chunks too small (misses the full answer), or embedding model misunderstanding your domain terminology. Fix with: 500-1000 token chunks with 100-token overlap, metadata filtering to narrow search scope, and hybrid retrieval (semantic + keyword) for domain-specific terms.

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