LLM Applications Development – FAQ

Question 1

What LLM services does STEPS offer?

Accepted Answer

We provide end-to-end LLM solutions including fine-tuning open-source models (such as Llama, Mistral, and others) on your proprietary data, building RAG (Retrieval-Augmented Generation) pipelines, developing custom AI-powered chatbots, and creating internal knowledge assistants that respond to queries specific to your organization's information.

Question 2

Can you fine-tune an LLM on our company's internal data?

Accepted Answer

Absolutely. We specialize in fine-tuning large language models on custom datasets so the model understands your business context. For example, we fine-tuned Llama 3 for Afreximbank to respond to inquiries specific to the bank's internal information, combining conversational queries with database-level retrieval.

Question 3

What industries can benefit from your LLM solutions?

Accepted Answer

Our LLM services apply across multiple sectors including banking and finance, insurance, healthcare, retail, e-commerce, energy, manufacturing, transportation, and communications. Whether you need automated document processing, intelligent customer support, or internal knowledge management, we tailor the solution to your industry.

Question 4

Do we need to share our sensitive data externally?

Accepted Answer

Data security is a priority. We can deploy LLM solutions on-premise or within your private cloud environment, ensuring your proprietary data never leaves your infrastructure. We also offer advisory services to help you define the right data governance strategy before any project begins.

Question 5

What is RAG, and why would we need it?

Accepted Answer

RAG (Retrieval-Augmented Generation) combines the power of LLMs with your existing knowledge base. Instead of relying solely on what the model was trained on, RAG retrieves relevant documents from your data in real time and uses them to generate accurate, context-aware responses. This is ideal for organizations that need up-to-date, factual answers drawn from internal repositories.

Question 6

How long does it take to implement an LLM solution?

Accepted Answer

Timelines vary depending on scope. A proof of concept can typically be delivered within 4 to 6 weeks. A full production-ready solution, including data preparation, fine-tuning, testing, and deployment, generally takes 2 to 4 months. We follow an Agile process with regular sprint reviews so you see progress throughout.

Question 7

What open-source models do you work with?

Accepted Answer

We work with a range of open-source LLMs including Llama 3, Mistral, Gemma, DeepSeek, and others. We evaluate which model best fits your use case based on performance, cost, language support, and deployment constraints, and we help you choose the most suitable tech stack for each project.

Question 8

Do you offer ongoing support after deployment?

Accepted Answer

Yes. We provide post-deployment maintenance, model retraining as your data evolves, performance monitoring, and continuous optimization. Our CTO-as-a-Service and dedicated team models ensure you always have access to expert support.

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