Azure RAG Chatbot
Azure-based RAG assistant for specialized enterprise queries with prompt optimization and retrieval tuning.
Cut information retrieval latency by 90% for specialized enterprise queries.
Read case study →RAG Systems
RAG systems for teams that need faster, more trustworthy knowledge retrieval
I design retrieval-augmented generation systems for teams that need grounded answers, lower latency, and better operational reliability than a thin wrapper around an LLM API can provide.
Who this is for
Product, support, operations, and expert teams that need faster access to internal knowledge, policy documents, or domain-specific information.
What problem this solves
Most RAG systems fail in the details: weak chunking, poor retrieval tuning, fragile prompting, or no operational loop for measuring answer quality and latency once the system meets real users.
What I build
I build and tune RAG systems that combine retrieval, prompting, evaluation, and operational feedback so they can support expert workflows instead of producing plausible but unreliable text.
- Knowledge assistants for internal expert queries
- Retrieval tuning for latency, precision, and grounding
- Prompt and context orchestration for higher answer quality
- Evaluation and instrumentation for production feedback loops
Relevant proof
The clearest proof here is work where retrieval quality and operational response time directly affected the usefulness of the system.
AWS Document Agent
Document assembly workflow with retrieval and contextual grounding layered into the generation process.
Produced grounded, structured outputs in under two minutes with policy guardrails.
Read case study →When to use this
- You have domain knowledge spread across documents, systems, or repositories.
- Users need grounded answers, not generic model output.
- Latency, retrieval quality, and trust in answers matter to adoption.
- You want a knowledge assistant tied to measurable workflows or usage patterns.
When not to use this
- You do not have usable or well-scoped source content yet.
- The task is better served by direct search or deterministic lookup alone.
- There is no owner for evaluation, answer review, or knowledge freshness.
Engagement shape
Discovery / design
Audit the knowledge sources, target questions, answer quality bar, and latency constraints.
Build / implementation
Implement ingestion, retrieval, prompt orchestration, evaluation flows, and the response layer that fits the use case.
Hardening / productionization
Instrument latency, monitor answer quality, and refine retrieval behavior based on production usage.
FAQ
What kinds of RAG systems do you work on?
I focus on enterprise and internal-use RAG systems where grounded responses, speed, and trust matter to day-to-day workflows.
Can you improve an existing RAG system instead of building one from scratch?
Yes. In many cases the highest-leverage work is tuning retrieval, context construction, prompts, and evaluation on top of an existing stack.
How do you measure whether a RAG system is actually better?
I look at retrieval quality, answer usefulness, latency, and the operational behavior of the system under real user queries, not only offline demos.
Do you only work with one cloud provider or framework?
No. The architecture depends on the use case and the existing environment, and I can work across the stack already in place.