Search Results: RAG

Turtosa engineers detail their journey creating a Retrieval-Augmented Generation chatbot using ChromaDB and OpenAI, overcoming LLM limitations like hallucinations and outdated knowledge. The implementation reveals critical insights about document chunking, context window management, and cost optimization for real-world AI applications.

Enterprise RAG deployments often balloon into costly optimization nightmares. By trimming vector‑DB queries, capping token context, and smartly reranking results, the team cut spending by nearly 40% while preserving accuracy.

As organizations increasingly rely on Retrieval-Augmented Generation systems, a subtle but critical challenge emerges: data drift. Engineers reveal how document extraction inconsistencies can silently degrade AI performance over time, and explore strategies to maintain stable ingestion pipelines.

Dropbox's Dash AI has transformed from a traditional retrieval-augmented generation (RAG) system into a full-fledged agentic AI, tackling the challenges of context overload in multi-tool environments. By consolidating retrieval tools, filtering for relevance via a knowledge graph, and delegating complex tasks to specialized agents, Dash achieves faster, more accurate decision-making. These context engineering strategies offer a blueprint for building efficient AI agents in enterprise settings.

As AI applications outgrow their initial generic responses, developers face a critical decision: fine-tune models for embedded expertise or leverage RAG for dynamic knowledge retrieval. This guide dissects when each approach excels across tasks like coding, summarization, and chatbots, balancing cost, accuracy, and flexibility to prevent costly missteps.

Voyage AI's new contextualized chunk embedding model, voyage-context-3, addresses a critical flaw in standard RAG pipelines by preserving inter-chunk relationships during vectorization. Demonstrated through a financial query use case, the technology elevated a crucial answer chunk from 8th to 1st place in retrieval rankings. This breakthrough promises significant accuracy gains for document-intensive applications like financial analysis and legal research.