LLM Development
LLM Development Services We Offer
Fine-tuning is one decision among several. We help you get the ones before and after it right too.
Base Model Selection & Benchmarking
We evaluate candidate base models against your actual use case and data — not a generic leaderboard — to find the right foundation before any training begins.
Fine-Tuning & Training Pipeline Design
We design and run fine-tuning pipelines using the method that fits your data volume and goals — full fine-tuning, LoRA, or instruction tuning.
Context & Tokenization Strategy
We design tokenization, context window, and chunking strategies specific to your domain's vocabulary and document structure.
Evaluation & Red-Teaming
We build evaluation suites specific to your use case — accuracy, hallucination rate, latency, and adversarial robustness — before the model ever reaches users.
How We Approach an LLM Development Engagement
A model that scores well on a public benchmark and one that performs on your actual task are often not the same model. We optimize for the second.
Use Case & Success Criteria Definition
We define exactly what the model needs to do and what "good" looks like in measurable terms, before comparing a single base model.
Base Model Evaluation
We benchmark candidate base models against your specific task and data, since general leaderboard performance often doesn't predict domain performance.
Data Preparation for Fine-Tuning
We prepare and structure your training data, including quality filtering and format alignment with the chosen fine-tuning method.
Fine-Tuning & Iteration
We run fine-tuning experiments, evaluating checkpoints against your success criteria and iterating on hyperparameters and data mix.
Evaluation & Red-Teaming
We test the model against held-out examples, edge cases, and adversarial prompts to catch failure modes before they reach production.
Deployment & Monitoring
We deploy the model into your infrastructure and set up monitoring for drift, cost, and output quality over time.
Why Businesses Choose Absolute Web for LLM Development
A lot of “LLM development” is really just prompt engineering with extra steps. We do the model-level work too.
Model-Level Engineering, Not Just Prompting
We work at the fine-tuning and training layer when a use case genuinely needs it, instead of defaulting to prompt engineering as the only lever.
Evaluation Built Around Your Use Case
We design evaluation suites specific to your domain and failure modes, not just reporting a generic public benchmark score.
Fine-Tuning Method Matched to Your Data
We choose between full fine-tuning, LoRA, and instruction tuning based on your data volume and goals, rather than a one-size-fits-all default.
Production-Minded From the Start
We design for the deployment constraints — latency, cost, infrastructure — that matter after launch, not just training-time metrics.
Technologies We Use
We leverage the cutting-edge of the AI technology stack to build robust agents:
Large Language Models (LLMs)
OpenAI
(GPT-4)
Anthropic
(Claude 3.5)
(Gemini)
Open-Source
(Llama 3)
Open-Source
(Mistral)
Frameworks & Orchestration
LangChain
LlamaIndex
AutoGPT
CrewAI
Programming Languages
Python
Node.js
TypeScript
Cloud & Infrastructure
AWS
Microsoft Azure
Google Cloud Platform
(GCP)
Pinecone
Weaviate
Milvus
Frequently Asked Questions
What is LLM development?
LLM development covers the model-level engineering work behind a large language model — base model selection, fine-tuning, training infrastructure, and evaluation — as distinct from prompt engineering or general AI consulting.
Do we need to fine-tune a model, or is prompting enough?
It depends on the use case — many problems are well served by prompting and retrieval alone, while others genuinely need fine-tuning for domain accuracy or consistency. We help assess which applies to yours.
Should we fine-tune an open-source model or use a closed API-based model?
It’s a trade-off between control, cost, and capability — open-source models offer more customization and data control, while closed models often offer stronger out-of-the-box performance. We evaluate both against your specific requirements.
How much training data do we need for fine-tuning?
It depends on the fine-tuning method — techniques like LoRA can work with far less data than full fine-tuning. We assess data sufficiency as part of base model evaluation.
How do you evaluate whether a fine-tuned model is actually better?
We build evaluation suites specific to your use case, testing accuracy, hallucination rate, and edge-case handling against the base model and any current process being replaced.
What is red-teaming and why does it matter?
Red-teaming means deliberately probing a model with adversarial or edge-case inputs to find failure modes before real users do — critical for anything customer-facing or high-stakes.
Can this integrate with a RAG system or agent framework we're already building?
Yes — LLM development often complements retrieval and agent architecture rather than replacing it; we design fine-tuning to work alongside your existing or planned RAG setup.
Is our data handled securely?
We follow data handling practices aligned with regional requirements (e.g., UK GDPR, Canada’s PIPEDA), including private deployment options, and recommend legal review for your specific obligations.
How long does an LLM development engagement take?
Most engagements move from use case definition to a deployed, evaluated model in 8–14 weeks, depending on data readiness and fine-tuning complexity.
How do I get started?
Book a free consultation — we’ll review your decisions, audiences, and existing data sources, and scope the engagement before any full project begins.