Fine-tuning and prompt engineering for reliable LLM products
Fine-tuning & Prompt Engineering as a repeatable delivery practice — applied with tooling, governance, and metrics your team can sustain.
Fine-tuning & Prompt Engineering — Overview
General-purpose models need domain adaptation — through prompts, RAG, or fine-tuning — before they are safe for customer-facing products.
Aqeeq evaluates approaches by cost, latency, accuracy, and maintainability — then ships the stack with evaluation harnesses and guardrails.
Prompt engineering — fast iteration, measured outcomes
Structured prompt libraries, eval datasets, and regression tests so prompt changes do not break production silently.
Key outcomes:
- Version-controlled prompts with A/B comparison
- Automated eval against golden question sets
- Cost and latency budgets per use case
Fine-tuning — when prompts are not enough
Custom model weights for domain vocabulary, tone, and task format — with data preparation and deployment pipelines.
Key outcomes:
- Training data curation from production logs (with PII handling)
- LoRA and full fine-tune options based on budget
- Serving integration with fallbacks and monitoring
Invoice processing: 18 hours to 35 minutes
Document AI extraction, validation, and approval routing integrated with ERP — measurable AP savings within the first quarter.
LLM adaptation services
From proof-of-concept to operated LLM features — with evaluation at every step.
Eval harness design.
Golden sets, rubric scoring, and human review loops for generative outputs.
RAG architecture.
Retrieval pipelines with chunking, embedding, and citation strategies.
Fine-tune pipelines.
Data prep, training jobs, and model registry integration.
Safety & guardrails.
Input/output filtering, PII redaction, and escalation paths.
Fine-tuning & prompts — common questions
When should we fine-tune vs use RAG?+
RAG suits dynamic knowledge and lower setup cost. Fine-tuning suits consistent format, tone, or domain language. We often combine both.
How do you prevent prompt regressions?+
Every prompt change runs against an eval suite with pass/fail thresholds before deployment.
Can you fine-tune on our proprietary data securely?+
Yes. We use isolated environments, data handling agreements, and on-prem or VPC options where required.
Move forward with fine-tuning & prompt engineering
Book a conversation — we will scope the engagement or tell you if another path is a better fit.