LLM Development
LLM Integration and Fine-Tuning Built for Production
From model selection through fine-tuning, evaluation, and LLMOps, Kovil AI handles the full LLM engineering lifecycle. GPT-4, Claude, Gemini, and Llama 3. Production-grade from day one.
What is LLM Development?
The Full Lifecycle: From API to Production LLMOps
LLM development is not just calling an API. It is systematically selecting the right model for your task, engineering prompts that are robust under varied inputs, building evaluation frameworks that measure quality, integrating with your data and tools, fine-tuning when general models are not domain-specific enough, and then operating the system in production with proper monitoring.
Most companies underestimate the engineering required to run LLMs reliably. Prompts break. Models degrade between versions. Costs spiral without monitoring. Output quality is invisible without evaluation. Kovil AI handles all of it.
Kovil AI's approach
We build the evaluation framework before the integration. If you cannot measure it, you cannot ship it. Every LLM system we deploy includes task-specific evals, prompt versioning, and a monitoring plan from day one.
$200B+
Enterprise LLM market projected by 2030 (Grand View Research 2025)
3x
Faster time-to-production for LLM systems with pre-built evaluation pipelines (internal Kovil AI data)
68%
AI engineering teams using Python + LLM APIs as their primary tech combination (Stack Overflow 2025)
What We Deliver
Six LLM Engineering Services
From first API call to production monitoring. We handle the complete LLM engineering lifecycle.
LLM API Integration
Production-grade integration of OpenAI, Anthropic, and Google APIs with streaming, error handling, retry logic, and cost monitoring.
Prompt Engineering
Systematic prompt design with structured outputs, chain-of-thought strategies, and regression testing against prompt changes.
Fine-Tuning (LoRA/QLoRA)
Domain-specific fine-tuning using parameter-efficient methods. We scope dataset construction, training, and evaluation.
Evaluation Framework
Task-specific evaluation before the integration. LLM-as-judge, RAGAS, and custom deterministic metrics for your use case.
Private LLM Deployment
Self-hosted Llama 3 or Mistral on AWS, GCP, or Azure using vLLM or TGI for high-throughput, privacy-compliant serving.
LLMOps and Monitoring
Prompt versioning, output quality monitoring, cost tracking, A/B testing for model updates, and alerting for degradation.
Model Selection Guide
GPT-4 vs Claude vs Gemini vs Llama 3
The right model depends on your task, data privacy requirements, and cost constraints. We run structured evals to confirm the selection before committing.
| Model | Provider | Context Window | Key Strength | Speed | Best For |
|---|---|---|---|---|---|
| GPT-4o | OpenAI | 128K tokens | General reasoning, vision, code | Fast | Versatile production workloads |
| Claude Sonnet | Anthropic | 200K tokens | Long documents, careful reasoning | Fast | Complex analysis, coding, long docs |
| Gemini 1.5 Pro | 1M tokens | Ultra-long context, multimodal | Medium | Tasks requiring massive context | |
| Llama 3 70B | Meta (open-source) | 128K tokens | Self-hosted, no data sharing | Varies (infra-dependent) | Private deployments, cost at scale |
| GPT-4o mini | OpenAI | 128K tokens | Speed, low cost | Very Fast | High-volume, cost-sensitive tasks |
Not Sure Which Model?
We scope the right LLM stack for your use case. Free.
Model selection, architecture review, and delivery scoping in one call.
Fine-Tuning
When to Fine-Tune vs When to Use RAG
Fine-tuning and RAG solve different problems. Most teams that ask about fine-tuning actually need RAG first.
Use RAG when...
- You need answers from a specific knowledge base
- Your data changes frequently
- You need citations and source traceability
- You want to avoid training costs
- You need to be live in 3-6 weeks
Fine-tune when...
- You need consistent style, tone, or output format
- Your domain has highly specialized vocabulary
- You need to reduce prompt length significantly
- Latency is critical and RAG adds too much overhead
- You need the model to follow complex task-specific instructions reliably
Case Study — Mortgage / FinTech
LLM-Powered Document Classification Platform for Secondary Mortgage Market
2-Phase
AI platform delivered end-to-end
Auto
document classification at intake
14 days
to first production shipment
FAQs
Common Questions About LLM Development
What is LLM development?
LLM development refers to selecting, integrating, fine-tuning, evaluating, and deploying large language models into production applications. It encompasses everything from initial model selection and API integration through fine-tuning on proprietary data, building evaluation frameworks, and maintaining models with LLMOps practices in production.
Should I use GPT-4, Claude, or Gemini?
It depends on your use case. GPT-4o excels at general reasoning, code generation, and vision tasks. Claude Sonnet is exceptional for long document processing, coding, and tasks requiring careful, nuanced outputs. Gemini 1.5 Pro is best when you need very long context windows. We help you run structured evaluations to choose the right model for your specific task rather than relying on general benchmarks.
When should I fine-tune an LLM vs using RAG?
Fine-tune when you need the model to adopt a specific style, format, or behavior consistently, or when you have a highly specialized domain with distinct vocabulary. Use RAG when you need the model to answer questions from a specific knowledge base and need responses to stay current with updated documents. For most enterprise use cases, RAG is the right first step. Fine-tuning is usually layer two.
What fine-tuning methods do you use?
We use LoRA (Low-Rank Adaptation) and QLoRA for parameter-efficient fine-tuning on consumer or cloud GPUs. For OpenAI models, we use their fine-tuning API. For Llama 3 and other open-source models, we fine-tune using the Hugging Face PEFT library with custom training pipelines on AWS or GCP.
How do you evaluate LLM performance?
We build task-specific evaluation datasets before the integration begins. For RAG systems we use RAGAS. For open-ended generation we use LLM-as-judge (a separate LLM grades outputs). For structured tasks we use deterministic metrics. The key is having a measurable definition of "working well" before any code is written.
Can you deploy LLMs on private infrastructure?
Yes. We deploy open-source models including Llama 3 and Mistral on AWS, GCP, Azure, or your own servers using vLLM or TGI for high-throughput serving. This is the right approach when data privacy regulations prevent you from sending data to third-party APIs.
What is LLMOps and do I need it?
LLMOps is the operational practice of running LLMs in production: prompt versioning, A/B testing of model updates, monitoring for output quality degradation, cost tracking, and incident response for model failures. If you are using an LLM in a production application, you need LLMOps. Without it, you will not know when your model stops working well.
How long does LLM integration take?
A basic LLM API integration with proper error handling, streaming, and a simple evaluation framework takes 2 to 4 weeks. A full production integration including fine-tuning, RAG, evaluation pipelines, and LLMOps monitoring typically takes 6 to 12 weeks depending on complexity.
From LLM Idea to Production System.
Model selection, integration, evaluation, fine-tuning, and LLMOps. Fixed-price delivery.
Book a Free LLM Architecture Call