Pramod Kumar Voola

Curator Note: 📝 Building an MCP server for this site. The protocol is elegant and the ecosystem is growing fast. This is the USB-C of AI tooling.

Curator Note: 📝 Prompt engineering was level one. Context engineering is the real game. Controlling what the model sees matters more than how you phrase the question.

Curator Note: 📝 My default LLM for everything from code to analysis. The extended thinking capability is unmatched for complex reasoning tasks.

Curator Note: 📝 RAG is table stakes for any production AI system. I prefer chunked retrieval with re-ranking over naive similarity search. The pattern is mature; the tuning is where the art lives.

Curator Note: 📝 AI-assisted coding is no longer optional. I use Claude Code as my primary tool and the agentic mode is a genuine force multiplier. Every engineer should be fluent in at least one.

Curator Note: 📝 Graph-based orchestration solves the control-flow problem that linear chains cannot. I use it for every workflow that needs loops or human-in-the-loop steps.

Curator Note: 📝 I run agent teams daily for parallel coding tasks. The productivity gain is real, but you need guardrails. Unreliable without structured outputs and checkpoints.

Curator Note: 📝 You cannot improve what you cannot measure. I log every chain execution in LangSmith. The eval framework alone justifies the investment.

Curator Note: 📝 Enterprise-ready model infrastructure. The knowledge base integration with S3 is particularly useful. Good for teams already invested in AWS.

Curator Note: 📝 Exciting but still early. Most multi-agent demos break down on real workloads. I am watching CrewAI and AutoGen closely but not betting production on them yet.