reliable ai assistant builder

The problem

LLMs frequently hallucinate policies, invent data when sources are missing, or take unauthorized actions because they lack clear operational boundaries. Without a formal evidence contract, agents become a liability in regulated or high-stakes environments.

What it does

• Generates a formal boundary contract defining approved knowledge sources and their confidence weights.
• Establishes an evidence taxonomy (Confirmed, Inferred, Unconfirmed, Unknown) with specific confidence floors.
• Defines a 4-tier authority hierarchy to manage system, domain, and user-level overrides.
• Builds structured response templates for handling missing, conflicting, or adversarial inputs.
• Codifies escalation routes and SLAs for human-in-the-loop intervention.

Why this beats prompting it yourself

Standard prompting relies on the model's "best effort" to be honest. This skill implements a systematic engineering framework that forces the model to categorize its own evidence before responding. It replaces vague "be helpful" instructions with a rigorous test-driven protocol that includes adversarial and ambiguity checks.

Use cases

• Deploying customer support agents for highly regulated insurance or legal policies.
• Building internal knowledge bots that must cite specific documentation and escalate when sources conflict.
• Creating enterprise AI "constitutions" to prevent prompt injection and unauthorized policy interpretations.
• Auditing existing agent configurations for reliability gaps and hallucination risks.

Known limitations

Does not handle UI copywriting or creative writing tasks. Requires an initial list of approved data sources to be effective.