LLM applications
LLM development services: what building and deploying an LLM application involves
A plain guide to the work behind an LLM application, and what to ask a provider before you sign. Microsoft guidance quoted as read on 10 November 2026.
LLM development services cover the work of turning a language model into a working application for your business. That usually means choosing a pattern (prompting, retrieval-augmented generation, fine-tuning, or a mix), building the data pipeline and the application around the model, testing it on real scenarios with the people who know the work, and deploying it with named owners, logging and guardrails. In Kriv AI's view, most of the effort is in the data, the evaluation and the operating model, not in calling the model.
definition
What do LLM development services include?
The model is one component. A working application also needs a defined business goal, the data the model should use, an interface or workflow where people use it, a way to test its answers, and a plan for who owns it after launch.
Providers differ in how much of this they cover. Ask whether the scope includes evaluation and operation, or stops at a demo.
explainer
Prompting, retrieval or fine-tuning: how is the pattern chosen?
Microsoft describes retrieval-augmented generation (RAG) as "an industry-standard approach to building applications that use language models to process specific or proprietary data that the model doesn't already know" (Azure Architecture Center, read 2026-11-10). That fits when the model must answer from your documents.
Prompting alone suits tasks where the model already has what it needs and you are shaping the instructions. Fine-tuning adapts a model's behaviour on your examples. Hybrids combine them. The right choice depends on your data, how often it changes and how the output will be checked.
Our page on LLM fine-tuning and custom models explains how we frame that choice.
process
What does the build involve?
For a retrieval application, Microsoft's guide lists the phases as preparation, chunking, chunk enrichment, embedding, information retrieval, and end-to-end language model evaluation (read 2026-11-10). In its data pipeline, documents are chunked, enriched with metadata, embedded and stored in a search index.
The same guide says that because of the number of steps and variables, "it's important that you follow a structured evaluation process" and that each step should be evaluated on its own while remembering that the end result is what users experience.
Sources: Microsoft Learn, Design and Develop a RAG Solution on Azure (https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/rag/rag-solution-design-and-evaluation-guide), read 2026-11-10.
operations
What happens after the build?
A deployed application needs an owner, monitoring and a way to correct it. Before you accept a delivery, ask who is named as owner, what is logged (prompts, responses and downstream actions), where the data is processed, and how a bad answer is reported and fixed.
Our page on LLM evaluation sets out how to test an application before and after release.
engagement
How Kriv AI works on LLM applications
Kriv AI states on its fine-tuning page: "We don't build foundation models. We specialize and govern them." We help you pick the pattern and implement it in a governed way, on AWS, Azure or your chosen platform. We never use your data for training models for other clients.
Our five-chapter method is Assess, Govern, Build, Validate and Operate, and each chapter leaves a written artifact you keep. These statements describe our delivery practices, not certifications.
Kriv AI scopes and prices work after a short discovery call. We do not publish rates on this page.
Straight answers
Frequently asked questions about LLM development services: what building and deploying an LLM application involves
What are LLM development services?
The work of turning a language model into a working application: choosing a pattern, building the data pipeline and application, testing, deployment and operation.
Do I need to fine-tune a model?
Not always. Microsoft describes RAG as an approach for applications that use data the model does not already know (read 2026-11-10). Fine-tuning is one option among prompting, RAG and hybrids.
What is RAG?
Retrieval-augmented generation: the application retrieves relevant material from your data and gives it to the model with the question. Microsoft's guide lists chunking, enrichment, embedding and retrieval among its phases (read 2026-11-10).
How should an LLM application be tested?
Evaluate each step on its own and the end result as users experience it, with real scenarios and the people who know the work. See our LLM evaluation page.
Will you use our data to train models for other clients?
No. Kriv AI never uses a client's data to train models for other clients.
What does it cost?
Kriv AI scopes and prices work after a short discovery call. We do not publish rates on this page.
Talk to the team that would do the work
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