RAG & KnowledgeIntermediate

RAG System Development

Build a production-ready Retrieval Augmented Generation system

3-6 weeks
2-4 people
6 tools
Key Tools
LlamaIndexPineconeQdrantOpenAI APIRagasLangfuse
Implementation Steps
  1. 1

    Design document ingestion pipeline with proper chunking

  2. 2

    Set up vector database (Pinecone for managed, Qdrant for self-hosted)

  3. 3

    Implement hybrid search combining semantic and keyword retrieval

  4. 4

    Use LlamaIndex for query routing and response synthesis

  5. 5

    Evaluate retrieval quality with Ragas metrics

  6. 6

    Add caching layer for frequently accessed content

  7. 7

    Monitor retrieval performance with Langfuse

Expected Outcomes
  • Accurate, grounded AI responses
  • Reduced hallucinations through retrieval
  • Scalable knowledge base integration
  • Measurable retrieval quality
Pro Tips
  • Chunk size matters - experiment with different sizes
  • Hybrid search often outperforms pure semantic search
  • Rerank retrieved results for better relevance
  • Monitor and log retrieval metrics in production
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