RAG Pipeline Development
Production Retrieval-Augmented Generation systems that answer questions accurately from your proprietary data — enterprise knowledge bases, clinical guidelines, regulatory documents, and internal APIs.
The Challenge
LLMs Hallucinate When They Don't Have the Right Context
Language models have impressive general knowledge but know nothing about your organisation's specific policies, your product documentation, your clinical guidelines, or the contents of your internal knowledge base. Without grounding in your proprietary information, LLMs fabricate answers with confident-sounding hallucinations. RAG (Retrieval-Augmented Generation) solves this by finding the most relevant content from your knowledge base and injecting it into the LLM's context before generation — so the model answers from evidence, not imagination. But building a RAG system that actually improves accuracy rather than just reducing hallucination requires expert knowledge of chunking strategy, embedding model selection, retrieval quality evaluation, and prompt design. A poorly built RAG system retrieves the wrong documents, provides misleading grounding, and can actually make hallucination problems worse.
Deliverables
RAG Development Capabilities
- Document ingestion pipeline — PDF, Word, Excel, HTML, Markdown, structured databases, and API sources ingested and parsed
- Chunking strategy design — semantic chunking, fixed-size with overlap, hierarchical chunking for nested documents, and parent-child chunk relationships
- Embedding model selection and evaluation — OpenAI text-embedding-3, Azure AI Search built-in vectorisation, Cohere Embed, and domain-specific fine-tuned embeddings for healthcare
- Vector store implementation — Azure AI Search hybrid (keyword + vector), Pinecone, Weaviate, Qdrant, and Cosmos DB for MongoDB with vector search
- Hybrid retrieval — combining dense vector search with sparse BM25 keyword search for improved recall on technical and clinical terminology
- Retrieval quality evaluation — RAGAS framework evaluation of faithfulness, answer relevancy, context recall, and context precision
- Re-ranking — cross-encoder re-ranker to improve precision of top-k retrieved chunks before LLM generation
- Multi-hop retrieval — decomposing complex questions and retrieving from multiple knowledge base sections
- Citation and source attribution — returning source documents and passages alongside answers for verifiability
- RAG observability — tracking retrieval quality, LLM generation quality, latency, and cost per query
Stack
RAG Technology Stack
Process
RAG System Development Process
A clear, predictable engagement model with no surprises.
Knowledge Base Assessment
Assess the document corpus — formats, volumes, update frequency, information density, and domain-specific terminology. Identify the question types the system needs to answer. Define evaluation criteria for what a correct answer looks like.
Ingestion & Indexing Pipeline
Build the document ingestion pipeline — parsing, cleaning, chunking, embedding, and indexing into the vector store. Implement incremental update pipelines for documents that change regularly.
Retrieval & Generation Design
Design and tune the retrieval strategy — hybrid search configuration, re-ranker selection, top-k settings, and prompt template design. Evaluate retrieval quality using RAGAS before connecting the LLM.
Evaluation & Quality Improvement
Run systematic evaluation against a representative question dataset. Score faithfulness, relevancy, and recall. Iterate on chunking, retrieval parameters, and prompts until quality targets are met.
Production Deployment & Monitoring
Deploy with retrieval quality monitoring, answer quality sampling, cost tracking, and alerting. Implement feedback collection so the system improves over time from real user interactions.
FAQ
Frequently Asked Questions
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