Adopt a "start simple" approach for AI development. Master prompting first. If that fails, use Retrieval Augmented Generation (RAG). Fine-tuning should be the last resort due to its complexity in deployment, serving, and keeping up with rapidly evolving base models.

Instead of expensive, static pre-training on proprietary data, enterprises prefer RAG. This approach is cheaper, allows for easy updates as data changes, and benefits from continuous improvements in foundation models, making it a more practical and dynamic solution.

Before considering expensive model fine-tuning, implement Retrieval-Augmented Generation (RAG). RAG dynamically retrieves information from a knowledge base to augment the prompt, solving most domain-specific problems efficiently. The recommended hierarchy is: Prompt Optimization -> Context Engineering -> RAG -> Fine-tuning.

Standard Retrieval-Augmented Generation (RAG) systems often fail because they treat complex documents as pure text, missing crucial context within charts, tables, and layouts. The solution is to use vision language models for embedding and re-ranking, making visual and structural elements directly retrievable and improving accuracy.

Retrieval Augmented Generation (RAG) uses vector search to find relevant documents based on a user's query. This factual context is then fed to a Large Language Model (LLM), forcing it to generate responses based on provided data, which significantly reduces the risk of "hallucinations."

Teams often agonize over which vector database to use for their Retrieval-Augmented Generation (RAG) system. However, the most significant performance gains come from superior data preparation, such as optimizing chunking strategies, adding contextual metadata, and rewriting documents into a Q&A format.

Vector search excels at semantic meaning but fails on precise keywords like product SKUs. Effective enterprise search requires a hybrid system combining the strengths of lexical search (e.g., BM25) for keywords and vector search for concepts to serve all user needs accurately.

The research on re-ranking that influenced Retrieval Augmented Generation (RAG) started with PhD student Rodrigo Nogueira's goal to create an AI researcher. He realized that before an AI could reason, it first needed a scalable way to navigate and retrieve relevant information from vast document sets.

Classic RAG involves a single data retrieval step. Its evolution, "agentic retrieval," allows an AI to perform a series of conditional fetches from different sources (APIs, databases). This enables the handling of complex queries where each step informs the next, mimicking a research process.