LLM Integration
I'm Muhammad Hamd, an AI engineer from Karachi, Pakistan, and I integrate large language models into real products for companies worldwide. Adding an LLM is easy to prototype and hard to get right in production, because latency, cost, hallucinations, and reliability all bite. I handle the full integration, which covers model selection, RAG, prompt and context engineering, orchestration, evaluation, and the cost and fallback controls that keep it dependable.
What this solves
- ✓An LLM prototype that works in a demo but breaks or costs too much in production
- ✓Hallucinated answers because the model isn't grounded in your data
- ✓No clear way to evaluate, monitor, or improve LLM output over time
- ✓Vendor lock-in or runaway API bills with no cost controls
What I build
Model selection & orchestration
Choosing and combining OpenAI, Anthropic, and open-source models per task, with routing and orchestration so each request uses the right model at the right cost.
RAG grounding
Retrieval-augmented generation that grounds answers in your documents and data, which keeps them accurate, current, and traceable instead of made up.
Prompt & context engineering
Structured prompts, context windows, and output schemas that make responses consistent and machine-usable rather than free-form text.
Evaluation & cost control
Test harnesses, monitoring, caching, and fallbacks that control quality, latency, and spend as you scale.
Tools & stack
Keep exploring
Frequently asked
Which LLM providers do you work with?+
I work with OpenAI, Anthropic, and open-source models such as Llama and Mistral, whether self-hosted or accessed through a provider. I pick based on your accuracy, latency, privacy, and cost needs, and I often route between them.
Should I use RAG or fine-tuning?+
Usually RAG first, because it grounds the model in your data, is cheaper to maintain, and updates instantly. Fine-tuning helps for style, format, or narrow tasks. I will recommend the right mix for your use case rather than defaulting to one.
How do you control LLM costs?+
Through model routing that sends easy tasks to cheaper models, plus caching, prompt compression, output limits, and monitoring. Together these usually cut spend significantly without hurting quality.
Can you integrate an LLM into an existing codebase?+
Yes. I regularly add LLM features to existing products and work alongside in-house teams through shared repos, code reviews, and clear documentation, so your team can maintain it after handoff.
Want llm integration for your team?
Tell me what you're trying to build. I'll reply with whether I can help and how I'd approach it.