1. Prompt logic is not a single prompt
Isolated prompt tricks do not scale. Reliable AI outputs require structured sequences where each prompt has a single responsibility, clear inputs, defined constraints, and human-in-the-loop review points.
Status: Static V1 — Structure in progress
Prompt logic for repeatable AI workflows
BCH designs prompt logic as part of a full workflow: what goes in, how the prompt sequence works, what comes out, and how the result is reviewed.
Developing structured prompt logic configurations, constraint definitions, and few-shot formatting patterns.
2. The BCH prompt framework
We configure prompt logic as systematic configuration templates. Our framework defines:
- Role and task definition
- Input requirements and data structure
- Context boundaries and operational constraints
- Step sequence and reasoning instructions
- Output formatting and schema rules
- Review checklist for human validators
- Iteration rules for handling errors
3. Inputs, constraints, and examples
To reduce hallucinations and inconsistencies, prompts must be fed with clean inputs, clear negative constraints (what NOT to do), and few-shot examples showing the desired style, reasoning path, and format.
4. Output format and review rules
Every prompt output must map directly to a human verification step. Outputs are structured (such as JSON or markdown tables) to align with checklists, making it simple for a team member to verify accuracy, style, and brand rules.
5. Use prompt logic inside a workflow
Prompt logic is never deployed as a standalone "AI assistant." It is always integrated into a broader workflow, supporting tasks like:
- Content workflows: Repurposing documents into posts, summaries, or briefs.
- Lead research workflows: Normalizing raw business data into structured lists.
- Proposal workflows: Formatting project scope and pricing outlines from briefs.
- Custom systems: Mapping custom business rules to predictable text formats.