Topic
All digests tagged Retrieval Augmented Generation (RAG)
Suggested route
Move from earlier context through the available material to the newest signals. This route is a reading aid, not a difficulty ranking.
Learn how to build robust AI agents by mastering hybrid search. This workshop covers combining semantic (vector) and lexical (BM25) search, using ESQL, and implementing advanced reranking techniques in Elasticsearch.
Read article
Agent memory is solved, but shared learning across agents is not. The solution requires an agent-native persistence layer (Meko) to promote private memories into governed, shared data packs.
Read article
The key architectural decision in enterprise AI is determining if knowledge belongs in the prompt (behavior), memory (facts), or weights (reflexes). This is not a ladder, but three distinct tools.
Read article
AI agents can simulate human behavior by combining LLMs with RAG, reflection, and planning. This allows organizations to run 'what-if' scenarios to predict customer and policy reactions before launch.
Read article
In the age of AI, data science skills—like handling noisy, non-deterministic signals—are more valuable than ever. Human judgment is critical for measuring AI output and designing robust RAG systems.
Read article
Two winning hack projects, LifeKit and Memo, demonstrate local AI on the GN100. They showcase advanced edge computing, using Neotron and Cosmos for offline survival guidance and wearable memory assistance.
Read article
Learn six essential AI concepts—from LLMs to RAG and Agents—using simple human analogies. Understand how modern AI systems work by connecting the 'brain' to its tools.
Read article
This presentation covers three aspects of search agents: evaluation, synthetic data generation using ORBIT, and trajectory analysis via tools like Hawkeye. It emphasizes that efficiency is as critical as accuracy.
Read article
Context engineering is critical for LLM agents. The talk reveals that compaction techniques can be detrimental because they invalidate prompt caches, forcing full-price token usage. Full history retention often wins on recall.
Read article
Learn how AI and Data Science combine into a single pipeline. We break down complex concepts like RAG, embeddings, and data cleansing using periodic tables to show how robust enterprise AI applications are built.
Read article
Memory harnesses solve context rot in long-horizon tasks by treating memory as a write-manage-read control loop, proving essential when answers fall outside the model's immediate context window.
Read article
Chunkless RAG uses document structure and AI agents instead of relying only on chunking and similarity search. Learn when structure-aware retrieval improves accuracy, context, and document understanding.
Read article
Learn how to build document agents that transform complex financial documents into decision-ready context. We cover LBO modeling, corporate lending, and mortgage servicing using API-driven workflows.
Read article
Learn how RAG solutions fail with complex documents and contradictory laws. We discuss practical techniques like clarification loops and robust document management to handle ambiguity in AI systems.
Read article
The talk demystifies AI agents by building one from scratch, demonstrating how LLMs, tools, and memory work together in a continuous loop to achieve goals.
Read article
This deep dive explores building an agentic retrieval harness that moves beyond simple RAG. It details combining hybrid search, local file system access (GP), and multimodal parsing to handle complex enterprise data.
Read article