Enrich LLM Context to Significantly Enhance Capabilities| Improve Your LLM Performance| Tech Edge AI

Описание: Your AI isn’t underpowered — it’s underfed. In this video, we explore how enriching your LLM’s context with metadata can massively boost performance, accuracy, and reasoning.

By leveraging additional data sources like filenames, timestamps, page numbers, and metadata, you can unlock the hidden potential of any large language model (LLM).

In this video, you’ll learn:
📈 Why LLMs need more than just text to perform well
🗂️ How to find and use metadata (filenames, folders, timestamps, etc.)
⚙️ Information retrieval techniques — static vs. on-demand enrichment
🤖 How LLMs can extract data dynamically using agentic functions
🌍 Use cases — metadata search, filtering, RAG optimization, and AI agent workflows
💡 Real-world applications — document AI, chatbots, and context-aware assistants

This concept — known as Context Engineering — is the secret behind advanced AI agents like Anthropic’s Deep Research and OpenAI’s RAG-based systems.

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