9 Dispatches from the engineering frontier — Deep dives, insights, and explorations
Most RAG pipelines fail not because the LLM isn't smart enough, but because the retrieval process is essentially "garbage in, garbage out." If you’re still splitting your docume...
Building persistent, multi-turn AI agents usually hits a wall when you move beyond simple chat loops. If your agent crashes midway through a complex tool-use sequence or a multi...
Most engineers treat AI agents as linear pipelines—a single prompt leading to a single completion. But when I started building production-grade automation for enterprise clients...
The era of "chatting" with an LLM to generate a single function is over. We have moved into the era of Swarm Orchestration, where autonomous agents collaborate to build, test, a...
Standard vector search often hits a wall when your data has deep, multi-hop relationships. If you ask a question that requires joining information across documents—like "How doe...
LLMs are great at chatting, but they’re essentially goldfish. They lose context the moment the request ends, and they can’t touch your database or call an API on their own. I sp...
Most RAG pipelines hit a wall once the vector database starts returning "okay-ish" results. You can tune your chunking strategy or swap embedding models all day, but often, the ...
Building a single AI agent that can handle complex workflows is often a recipe for hallucination and brittle code. Lately, I’ve shifted my architecture toward multi-agent system...
Tracking objects across hours of raw CCTV or drone footage used to be a manual nightmare. Last month, I had to build a pipeline that turns raw video streams into structured, que...