Agentic RAG: When Should It Retrieve?
Naive RAG retrieves on every turn, even when it shouldn't. How agentic RAG lets the model route, grade its own context, and re-retrieve, plus the tradeoffs.
#Agents(8)
Managing the Context Window in Long Agent Runs
An LLM agent that runs long enough fills its context window and starts to slow or fail. How to prune, compact, and offload context so agents keep going.
Prompt Caching: Cut LLM Cost and Latency
Prompt caching reuses a request's prefix to cut LLM cost and latency. How the prefix match works, where to put the breakpoint, and the silent cache misses.
Tracing an LLM Agent with OpenTelemetry
An LLM agent request hides where the time and tokens went behind one flat log. Trace it with OpenTelemetry spans, the GenAI conventions, and where it breaks.
Indirect Prompt Injection in RAG Systems
A RAG copilot reads tickets and logs, so whoever writes them can plant instructions in the prompt. How indirect prompt injection works and how to contain it.
Build an MCP Server for Your LLM Agent
MCP standardizes how LLM agents reach your tools and data. A hands-on guide to building an MCP server in Python, picking a transport, and where it breaks.
Running LLM-Generated Code in a Sandbox
An LLM that writes and runs code needs real isolation, not a try/except. How to sandbox AI-generated code with E2B microVMs, and the failure modes.
How an LLM Agent Tool-Calling Loop Works
A practical look at the agent loop behind LLM tools: how the model asks to call a tool, your code runs it, and the result feeds back until the answer is done.