Principles of Safe Prompt Design
Ensure every prompt reflects organisational values, regulatory boundaries, and domain-specific constraints.

Key Focus Areas:
– Embedding disclaimers, scoping, and guardrails into system prompts
– Reducing ambiguity and open completions
– Use of constraint-driven prompting for factuality and scope adherence
– Aligning with FCA Consumer Duty, GDPR, and SMCR expectations

Minimising Indirect Harm and Group Bias
Crafting prompts that avoid stereotyping, group harm, or unintentional bias amplification.

Key Checks:
– Language auditing for bias triggers or leading phrasing
– Prompt reframing to ensure fairness across demographics
– Adversarial testing to simulate misuse or identity-specific distortion
– Proxy bias analysis based on prompt-content correlation

Understanding Jailbreaks, Leaks, and Prompt Injection

LLMs are easily manipulated. Through our LLM prompt writing course, We train teams to write resilient prompts and test for adversarial inputs.

Key Defences:
– Prompt structure patterns to resist jailbreak attempts
– Use of “prompt wrapping” and role control
– Detection of prompt injection and prompt leaking behaviours
– Case studies: Claude, GPT-4, Mistral jailbreak simulations

Reducing Hallucinations in Enterprise Contexts
Designing prompts that elicit grounded, verifiable, and domain-aligned outputs.

Key Tactics:
– Use of retrieval-augmented prompts (RAG pattern)
– Embedding source verification and citation instructions
– Scoping prompts tightly to policy documents, procedures, or manuals
– Test outputs for alignment with internal data or regulatory rules

Making Prompts Audit-Ready and Traceable
All prompts and outputs used in regulated contexts must be traceable, reviewable, and justifiable.

Key Tools:
– Prompt + output chaining documentation
– Logging architectures (e.g. LangChain, Guardrails.ai)
– Versioning of prompt iterations
– Linking prompts to risk acceptance criteria and board oversight