Prompt Engineering & Governance
Enterprise
Prompt Engineering
We transform prompting from an art into an engineering discipline. Implement rigorous governance, complex reasoning chains, and automated regression testing for your Foundation Models.
Prompt Management
Prompt Registry & Governance
We treat prompts as software artifacts. Our Prompt Registry enforces version control, role-based access, and automated safety checks. No prompt reaches production without passing a Governance Gate.
RBAC Enforcement
Only "Lead Prompt Engineers" can approve versions. Developers can only draft.
Template Management
Standardized Jinja2 templates allow dynamic injection of context while maintaining structure.
Version History
Repo: support-agent-prompts
Prompt Content
You are an empathetic customer support agent for React Digi.
<guardrails>
- Deny competitor mentions
- Mask PII
</guardrails>
Answer helpfuly.Compliance Check:
PASSED Performance Enhancement
Prompt Optimization
Basic prompting isn't enough for enterprise apps. We implement advanced techniques like Chain-of-Thought (CoT) to improve reasoning and Structured Output Enforcement to ensure the LLM returns clean, parsable JSON for downstream systems.
Input Prompt
Parsable Data
"Extract items to JSON format with schema { item, qty, attr }."
Model Response
[
{ "item": "apple", "qty": 2, "attr": null },
{ "item": "shirt", "qty": 1, "attr": "red" }
] Booking Assistant
Online
Starting conversation...
Type a message...
Interactive Systems
Stateful Conversation Design
Great AI assistants don't just generate text; they maintain State. We architect systems using techniques like Slot Filling and Intent Recognition to guide users through complex multi-turn workflows (e.g., Booking, Troubleshooting) without losing context.
System InternalsLive Trace
Detected Intent
Waiting...
Context Slots
Empty context...
State persistence enabled via Redis/DynamoDB
Classifier
Thank You
Escalate
Complex Prompt Systems
Prompt Chaining & Branching
Single prompts fail at complex tasks. We engineer Prompt Chains (using LangChain or Step Functions) that break tasks into steps. We implement Conditional Branching where the output of one prompt (e.g., "Sentiment Analysis") determines which prompt runs next.
Logic Example
1. classify_sentiment(user_input)
2. if sentiment == 'NEGATIVE':
return escalate_to_human_prompt()
3. else:
return generate_thank_you_prompt()
Quality Assurance
Automated Regression Testing
Never deploy a prompt blindly. We build Automated Test Suites that run your prompts against a "Golden Set" of inputs and expected outputs. We use Semantic Similarity Metrics (Cosine Similarity) to grade responses automatically, ensuring new versions don't break existing functionality.
Test Results
Pass Threshold: > 0.80
| Input Case | Actual Output | Similarity | Status |
|---|---|---|---|
| Click "Run Regression Test" to start... | |||