Rahul Sharma

AI Agents & Automation

How do you prevent infinite looping and state lockouts in multi-agent LLM tool execution workflows?

Prevent infinite loops and state lockouts in multi-agent LLM workflows using hard execution limits, stateful cycle detection, tool idempotency, and arbitration mechanisms.

AI Agents & Automation

How to handle non-deterministic JSON output formatting errors from LLM tools in production?

Implement robust parsing, strict schema validation, and intelligent self-correction/retry mechanisms to reliably handle non-deterministic JSON output from LLMs in production.

RAG & Vector Databases

What are the trade-offs between pgvector, Pinecone, Qdrant, and Milvus for production RAG?

Choosing between pgvector, Pinecone, Qdrant, and Milvus for production RAG involves trading off simplicity, scalability, operational overhead, and advanced features like hybrid sea

Prompt Engineering & LLMs

What is the role of Few-Shot Prompting when fine-tuning is not economically viable?

Few-shot prompting delivers task‑specific results without fine‑tuning by using a few curated examples, saving compute and data costs.

System Prompts & Guardrails

How to implement runtime guardrail frameworks like NeMo Guardrails or Guardrails AI?

Inject guardrails before and after LLM calls using YAML policies for NeMo and Python validators for Guardrails AI, and explicitly handle block actions.

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