Vector embeddings can fail to connect related documents if they lack sufficient semantic similarity. A knowledge graph, by following explicit, human-created links, can fix these specific structural misses. It successfully retrieves relevant context that even a deep vector search (k=15) consistently fails to find, proving its unique value for structurally related data.

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.

Knowledge graph-based retrieval is not a replacement for deep vector search but a complement that solves different failure modes. While a larger context window in vector search yielded better overall results, the graph walk succeeded in connecting documents linked by human logic but missed by semantic similarity, proving that the two methods are not mutually exclusive.

Splitting documents by a fixed token count is a common and costly error in RAG pipelines. According to a cited study, switching to an adaptive, meaning-based chunking strategy (e.g., by paragraph) can increase fully accurate answers from 13% to 50%.

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."

Implementing an agentic loop, where a model can search multiple times, is not a universal solution and is highly model-dependent. While Anthropic's Haiku model used the extra searches to improve recall, Amazon's Nova Light failed to reformulate its queries effectively, performed slightly worse, and wasted its budget, highlighting significant performance trade-offs.

Vector similarity does not equal relevance. A lightweight re-ranker model, placed between retrieval and generation, rescores search results for usefulness. This significantly improves performance on ambiguous queries and is one of the highest-impact additions to a RAG pipeline.

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.

Most production RAG systems fail not because of the LLM or prompt, but due to poor document parsing, chunking, and indexing. Teams mistakenly debug the generation layer when the foundational data processing is the true root cause of poor performance.