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Vector databases

The 100-Tool Agent Is a Trap - Sohail Shaikh & Ankush Rastogi, Prosodica

The 100-Tool Agent Is a Trap - Sohail Shaikh & Ankush Rastogi, Prosodica

This session unveils the "Fat Agent" trap in LLM agent architectures, where loading an entire tool catalog into the system prompt causes severe latency, cost, and accuracy problems. It presents the Semantic Tool Router pattern, a practical solution leveraging Just-in-Time Context Injection and vector search (RAG for tools) to dynamically provide only relevant tools, dramatically reducing prompt size, enhancing performance, and improving reliability across frontier models like GPT-4o and Gemini 2.0.

Benchmarking semantic code retrieval on Claude Code — Kuba Rogut, Turbopuffer

Benchmarking semantic code retrieval on Claude Code — Kuba Rogut, Turbopuffer

A detailed benchmark analysis comparing raw Claude Code's performance with windowed grep and Turbopuffer's semantic search for code retrieval in LLM agents. The study reveals significant improvements in file precision (65% to 87%) and reduced wasted reads (1 in 3 to 1 in 8) with semantic search, while highlighting the importance of the agent's understanding of when to use retrieval tools.

What is Multimodal RAG? Unlocking LLMs with Vector Databases

What is Multimodal RAG? Unlocking LLMs with Vector Databases

A technical breakdown of three distinct approaches for implementing Multimodal Retrieval-Augmented Generation (RAG), moving from simple text conversion to fully integrated cross-modal systems. The discussion covers the architecture, trade-offs, and capabilities of each method.

7 AI Terms You Need to Know: Agents, RAG, ASI & More

7 AI Terms You Need to Know: Agents, RAG, ASI & More

A deep dive into seven essential AI concepts shaping the future of intelligent systems, including Agentic AI, RAG, Mixture of Experts (MoE), and the theoretical frontier of Artificial Superintelligence (ASI).

The 2025 AI Engineering Report — Barr Yaron, Amplify

The 2025 AI Engineering Report — Barr Yaron, Amplify

Barr Yaon of Amplify Partners presents early findings from the 2025 State of AI Engineering survey, covering LLM usage, customization techniques like RAG and fine-tuning, the state of AI agents, key challenges like evaluation, and community perspectives on the future of AI.

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