Which LLM should we use — OpenAI, Claude, or Gemini?

Model selection depends on your use case. GPT-4o excels at structured output and function calling. Claude 3.5 Sonnet is outstanding for long documents and nuanced reasoning. Gemini 1.5 Pro offers the longest context window. We run benchmark tests on your specific task before committing to a model.

What is RAG and when do we need it?

RAG (Retrieval Augmented Generation) grounds the LLM's responses in your own documents and databases, preventing hallucination. You need RAG when the model needs to answer questions about your internal data, products, policies, or anything not in its training data.

How do you prevent LLM hallucinations?

We implement a multi-layer approach: structured output parsing with Zod or Pydantic schemas, confidence scoring, RAG with source citations, human-in-the-loop escalation for low-confidence outputs, and automated evaluation against ground truth test sets.

Can you self-host an LLM on our own servers?

Yes. We deploy open-source models (Llama 3, Mistral, Mixtral) on your own AWS, Azure, or on-premises infrastructure. This eliminates data sharing with API providers and removes per-token costs for high-volume use cases.

How do you manage LLM costs at scale?

We implement semantic caching (identical or similar queries reuse previous responses), request batching, model tiering (cheaper models for simple tasks, expensive models only for complex reasoning), and real-time cost dashboards.