Focus & Stack
Fine-tuning, Prompt Engineering, Retrieval-Augmented Evaluation, Llama, Mistral, GPT, Claude, PyTorch
We close the gap between general AI and your specific needs. Sometimes that means fine-tuning a model. Often it means smarter prompt engineering, retrieval design, or structured outputs. We use the lightest technique that hits the quality target, and only invest in heavier approaches when simpler ones fall short.
The adaptation spectrum
Prompt Engineering
Careful system prompt design with domain context, terminology guides, output specs, few-shot examples. Often resolves the issue without touching the model.
Few-Shot & In-Context Learning
Representative examples in the prompt. Dynamic selection of the most relevant examples for each input.
Retrieval-Augmented Domain Context
Terminology glossaries, style guides, business rules retrieved and included at query time. Change the documents, change the behaviour.
Structured Outputs
JSON schemas, enum values, template-based text. Constraining the output space eliminates many domain adaptation issues.
Fine-Tuning
Training on domain-specific examples. Appropriate for high-volume, highly specific tasks where prompt length gets costly or behaviour is hard to describe but easy to demonstrate.
Custom Model Training
Task-specific models for classification, entity recognition, or specialised tasks where a dedicated model outperforms a general LLM.
How it works
Define Quality Targets—Step 1
What does "good enough for production" look like?
Build Evaluation Benchmark—Step 2
Input-output pairs with domain experts, including edge cases.
Start Light—Step 3
Begin with prompt engineering, escalate only if metrics require it.
Measure Against Baseline—Step 4
Every change validated. Improvement must be statistically significant.
Regression Testing—Step 5
Ensure adaptations don’t degrade performance on related tasks.
Deliverables
What you get
- Adapted AI capability deployed for production
- Comprehensive evaluation results vs. baseline
- Documentation of approach (prompts, examples, fine-tuning data, retrieval config)
- Benchmark datasets for ongoing evaluation
- Monitoring and maintenance guide
Case Study
Hashlock AI Auditor
RAG over a curated vulnerability knowledge base — specialising a general foundation model for a narrow, high-stakes domain (Solidity, Rust, Vyper auditing).
Read case study
AI outputs good but not good enough for production?
We’ve seen teams spend months fine-tuning when better prompting would have solved it in days. And vice versa. We help you make the right call, then execute it properly.