LLM & AI Agent Development
LLM application development puts a language model inside a useful product or workflow. An AI agent adds a controlled set of tools the model can select. I work with Python, LangGraph, retrieval-augmented generation (RAG) and APIs, with portfolio projects covering feedback processing and conversational voice AI.
By Anurag Srivastav · New Delhi, India · Open to remote projects
Discuss your projectChoose the smallest useful system
A classification or extraction task may need a single model call with a defined output format. A knowledge assistant may need retrieval over approved documents. A task that requires several dependent actions may benefit from an agent with explicit states and a limited tool set.
Start with the user’s question and the action the system must support. Giving an agent more tools does not automatically improve the result. Specify when it should ask for clarification, refuse an unsupported action or hand a task to a person.
When RAG is relevant
Retrieval-augmented generation supplies selected source material to a model when it answers. It is useful when an application must work with a defined document collection rather than relying only on the model’s general knowledge. The source documents, access rules and update process are part of the application design.
Retrieval does not guarantee that an answer is correct. Evaluate whether the right material was retrieved and whether the answer actually follows it. A useful knowledge assistant can show its sources and say when the available material does not support an answer.
What to test before launch
Prepare representative questions, difficult examples and expected outcomes before selecting a model. Evaluate task success, unsupported statements, inappropriate tool use, latency and cost. Review failures individually rather than relying only on an average score.
For an agent connected to business systems, separate read access from write actions and identify which actions require approval. For a voice application, also test interruptions, unclear speech and connection failures. These checks should match the real environment where people will use the system.
Related work from my portfolio
BMRC Hospital AI Feedback Agent
Enterprise AI workflow that processes multilingual patient feedback, runs sentiment analysis, routes tickets automatically and sends Gmail notifications.
Tools: n8n, OpenAI, Supabase, Gmail API
Explore the portfolio projectsAI Voice Agent
Real-time conversational voice AI with streaming audio, OpenAI integration and WebSocket communication built for production-ready latency.
Tools: OpenAI, WebSockets, Streaming Audio
Explore the portfolio projectsQuestions about LLM and AI agent development
Do I need an agent or a simpler LLM application?
Use the simplest design that meets the task. A fixed classification or drafting step may only need one model call. Consider an agent when the task needs decisions between several allowed tools or steps, and those decisions can be evaluated.
Does RAG eliminate hallucinations?
No. Retrieval can provide relevant evidence, but both retrieval and generation can fail. Source citations, evaluation examples and an explicit unsupported-answer behavior are still useful.
How do I start an LLM development project?
Bring the intended user journey, sample questions, approved data sources and a description of any actions the system should take. We can then discuss a bounded pilot and how to judge its outputs before connecting it to production systems.
Tell me what you want to build
Share the problem, your existing tools and the outcome you need. We can discuss the scope, constraints and a practical starting point.
Book a project discussionContact Anurag