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Prompt Engineering

Chaincode helps businesses and product teams craft, test, and systematise high-performance prompts that make LLM-powered features reliable and consistent in production. We treat prompts as code — version-controlled, rigorously tested, and continuously improved based on real output data.

From system prompt architecture for customer-facing AI features to output evaluation pipelines and cost-optimisation strategies, we cover the full prompt engineering lifecycle. Every production prompt is also red-teamed for injection vulnerabilities and hardened against adversarial inputs before it goes live.

About this service

Craft, test, and systematise high-performance prompts that make your LLM-powered products reliable, consistent, and cost-efficient in production.

Key Features

01

Prompt strategy design: zero-shot, few-shot, chain-of-thought, and structured output patterns

02

System prompt architecture for production LLM applications

03

Prompt versioning, A/B testing, and regression testing frameworks

04

Output evaluation pipelines: automated scoring for accuracy, tone, and safety

05

Cost optimisation: reduce token usage without sacrificing output quality

06

Jailbreak and prompt injection hardening for public-facing AI features

07

Prompt library documentation and team handover packs

Why Trust Chaincode

We treat prompts as code: version-controlled, tested, and continuously improved

Model-agnostic expertise across GPT-4o, Claude 3, Gemini, and open-source LLMs

Proven evaluation frameworks so you can measure prompt quality objectively

Security-first: every production prompt is red-teamed for injection vulnerabilities

Our Process

01

Task analysis: define the exact input/output contract and success criteria

02

Baseline prompts: write initial candidates using established engineering patterns

03

Evaluation setup: build a test dataset and automated scoring pipeline

04

Iteration cycles: test, score, identify failure modes, and refine prompts

05

Hardening: adversarial testing for prompt injection, hallucination, and refusals

06

Handover: documented prompt library, versioning system, and team training

Frequently Asked Questions

More than ever. Smarter models respond even more predictably to well-structured prompts. The difference between a naive prompt and an engineered one in production is often 30–50% improvement in output reliability.
Yes. We audit your existing prompts, identify failure patterns from your logs, and systematically improve them — often achieving better results with fewer tokens and lower costs.
Both. We can engineer prompts for base models, fine-tuned models, and hybrid RAG architectures. We also advise on when fine-tuning is worth the investment versus continued prompt iteration.

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