ai-llm-engineering

Build a RAG pipeline by combining document chunking, embedding creation, vector search, and LLM-based answering to generate answers with traceable source citations. This approach automates orchestration of embeddings, vector databases, and LLMs for reliable, citation-backed results.

Retrieval-augmented generation uses vector databases to store document embeddings, retrieving relevant chunks on demand to answer questions with cited sources. This mechanism retrieves up-to-date data dynamically, ensuring LLM outputs are grounded in specific source material.

Evaluate LLM outputs in a RAG workflow by applying an integrated RAGEvaluator framework and safety guardrails. These checks validate faithfulness, relevance, and source coverage to govern the workflow and ensure reliable results.

Yes, you can use RAG for codebase analysis and knowledge extraction from large corpora. The pipeline retrieves up-to-date data on demand, applying embedding creation and vector search to extract knowledge and answer codebase QA queries.

Limitations of retrieval-augmented generation for AI-assisted research include the need for carefully configured chunking strategies and embedding models. Poor source coverage or reranking can affect faithfulness, making the integrated RAGEvaluator guardrails essential for verifying results.