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All digests tagged RAG
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The future of enterprise AI lies in long-running agents that automate document workflows. We explore the advanced document context layer, covering specialized OCR, agent harnesses, and optimizing models for cost and accuracy.
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Learn how a Context Engine moves beyond vector search to handle structural, relational, and temporal data queries, significantly improving AI agent reliability and cutting token burn.
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For enterprise data, simple file traversal fails. We need an agentic 'harness' that combines hybrid search, metadata filtering, and specialized parsing (LlamaParse) to ground answers from massive, multimodal corporate documents.
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EmbeddingGemma 2 is a lightweight, open model for natively multimodal embeddings. It maps text, images, video, and audio into a single unified embedding space, enabling powerful, fully offline search and RAG pipelines on edge devices.
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Learn how to scale AI chatbots from 40 files to 80,000+ documents. This talk covers AI-assisted metadata tagging, sensitive data detection, and maintaining data quality (freshness, conflicts) to ensure reliable RAG context.
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Traditional monitoring is insufficient for GenAI. Learn why Cost, Safety, and Quality metrics are essential for building robust, production-ready LLM applications.
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The talk details building the document context layer for AI agents, arguing that RAG requires a three-layer platform: parsing, semantic storage, and repeatable workflows. It addresses the complexity of parsing PDFs and introduces the ParseBench benchmark.
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AI agents need a 'context engine' to move beyond simple information retrieval. Learn how to fix the context gap, avoid the 'curated context trap,' and achieve massive token and time savings in your CI/CD pipelines.
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Fine-tuning LLMs can create a 'calcification tax,' leading to architectural lock-in. Migrating to an agentic framework using skills and context reduced fix deployment time from weeks to under an hour.
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Hallucination is when an AI system confidently gives information that's totally incorrect. As agents take action, grounding them with tools and defining scope are critical design choices.
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Martin Keen explains how LLM fine tuning compares to RAG, LoRA, and generative AI techniques, outlining when custom model training is still necessary in modern AI workflows.
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This roadmap guides transitioning from Data Science to AI Engineering, emphasizing the shift from notebooks to structured Python projects using FastAPI, Pydantic, Docker, and PostgreSQL for production-ready systems.
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