Building a basic Retrieval-Augmented Generation (RAG) prototype is easy, but scaling it into a reliable, production-ready AI application is incredibly challenging. If your AI chatbot or agent is ignoring context, providing irrelevant answers, or hallucinating facts, your pipeline needs expert optimization.

I specialize in diagnosing bottlenecks and debugging AI pipelines to ensure your Large Language Models (LLMs) stay grounded in your actual data. I will audit your current architecture and implement advanced techniques to drastically improve precision, recall, and answer relevance.

Advanced Chunking & Ingestion: I will fix poorly split documents by implementing semantic chunking strategies that preserve context, ensuring your Vector DB stores meaningful data.

Retrieval Optimization: I will upgrade your search pipeline by implementing Hybrid Search (Keyword + Vector) and multi-stage retrieval with Cross-Encoder Re-ranking to surface only the most relevant context.

Prompt Engineering & Generation Tuning: I will refine your system prompts and implement strict context-window management so the LLM strictly adheres to retrieved facts and refuses to guess.

Evaluation & Metrics: I will implement evaluation frameworks (like RAGAS) to quantitatively measure your pipeline's accuracy, faithfulness, and answer relevance before and after optimization.

Tech Stack & Expertise: LangChain, LlamaIndex, OpenAI, Anthropic, HuggingFace, Pinecone, Weaviate, ChromaDB, and Python.

Whether you need a quick technical audit or a complete pipeline overhaul, let's collaborate to build an AI system you and your users can actually trust. Send an inquiry to discuss your specific architecture!