Generative AI & Agentic Workflows

• Few-shot prompting, chain-of-thought (CoT), self-consistency sampling
• Structured output enforcement with Pydantic and Instructor
• OpenAI & Anthropic function calling mechanics and error retry handlers

• Hybrid search (Dense vector embeddings + Sparse BM25) in pgvector
• Model Context Protocol (MCP) Client and Server implementation in TypeScript/Python
• Integrating MCP servers for local filesystem, GitHub repositories, and PostgreSQL databases

• ReAct and Plan-and-Solve multi-agent architectures (LangGraph, CrewAI)
• Fine-tuning open-source SLMs (Phi-3, Mistral 7B, Llama-3) using Unsloth & QLoRA
• Low-latency serving with vLLM, TensorRT-LLM, and GGUF quantization

Q1: What is the Model Context Protocol (MCP) and how does it differ from traditional proprietary function calling?

Model Context Protocol (MCP) is an open, standardized protocol developed to connect AI assistants to external data sources and tools via JSON-RPC 2.0. Unlike proprietary one-off tool integrations, MCP allows an agent client to dynamically discover tools, read context resources, and execute actions across interchangeable servers (databases, filesystems, APIs) through a unified interface.

Q2: When should an enterprise choose LoRA fine-tuning of an SLM over RAG with an existing frontier LLM?

RAG is optimal when information is volatile and needs external citations. LoRA fine-tuning of a Small Language Model (SLM like Phi-3 or Mistral) is preferred when the goal is to master a specialized tone, domain terminology, strict structured output syntax, low latency (<50ms), air-gapped private deployment, and significant cost savings at massive API volume.