aibot88 Prompt Engineering
System prompt optimization specialist for BotFacebook.
MCP Tools Available
- context7:
query-docs- Get latest OpenAI/Anthropic prompt engineering docs - sentry:
search_issues- Find AI response quality issues - claude-mem:
search,get_observations- Search past prompt iterations
Memory Search (Before Starting)
Always search memory first to find past prompt iterations and improvements.
Recommended Searches
# Search for prompt changes
search(query="prompt optimization", project="bot-fb", type="feature", limit=5)
# Find A/B test results
search(query="prompt test", project="bot-fb", concepts=["trade-off"], limit=5)
Search by Scenario
| Scenario | Search Query |
|---|---|
| Improving prompts | search(query="prompt improvement", project="bot-fb", type="feature", limit=5) |
| Injection prevention | search(query="prompt injection", project="bot-fb", type="bugfix", limit=5) |
| Quality issues | search(query="AI response quality", project="bot-fb", concepts=["problem-solution"], limit=5) |
Quick Start
เมื่อปรับ prompt ให้คิด:
- เป้าหมายคืออะไร? → Define success criteria
- Context ครบไหม? → Add necessary context
- วัดผลได้ไหม? → Create test cases
Core Principles
| Principle | Description |
|---|---|
| Be Specific | ระบุงานชัดเจน ไม่กว้างเกินไป |
| Provide Examples | ให้ตัวอย่าง 2-3 คู่ input/output |
| Set Boundaries | กำหนดข้อห้ามชัดเจน |
| Define Persona | ระบุบทบาท น้ำเสียง ภาษา |
Full patterns & templates: See PROMPT_PATTERNS.md
A/B Testing
| Metric | Description |
|---|---|
| Response accuracy | ตอบตรงคำถามไหม |
| Tone consistency | น้ำเสียงคงที่ไหม |
| User satisfaction | ลูกค้าพอใจไหม |
| Escalation rate | ส่งต่อคนบ่อยไหม |
Test framework & analysis: See TESTING_GUIDE.md
Security (Injection Protection)
| Strategy | Purpose |
|---|---|
| Input Sanitization | Remove dangerous chars |
| Delimiter Usage | Separate user input |
| Pattern Detection | Detect attack patterns |
| Output Validation | Check for leaks |
Full security guide: See SECURITY.md
Common Tasks
| Task | Steps |
|---|---|
| Create prompt | Goal → Persona → Knowledge → Examples → Constraints |
| Improve prompt | Collect failures → Identify pattern → Fix → A/B test |
| Debug response | Check context → Check examples → Check model |
| Secure prompt | Delimiters → Sanitize → Detect → Validate |
Debug Checklist
- Knowledge base complete?
- Examples representative?
- Boundaries appropriate?
- Model suitable for task?
Second AI Prompt Patterns
Second AI checks use specialized prompt templates for verification:
| Service | Prompt Pattern | Purpose |
|---|---|---|
FactCheckService |
KB context + claim extraction | Verify factual accuracy against knowledge base |
PolicyCheckService |
Policy rules + response analysis | Check business rule compliance |
PersonalityCheckService |
Brand guidelines + tone analysis | Ensure brand consistency |
UnifiedCheckService |
Combined single-call prompt | All checks in one LLM call (2+ checks) |
PromptInjectionDetector
app/Services/SecondAI/PromptInjectionDetector.php runs BEFORE Second AI checks:
- Pattern-based detection (regex) for common injection attempts
- Detects role-playing attacks, system prompt extraction, instruction override
- Lightweight (no LLM call) — runs on every incoming user message
Key Files
| File | Purpose |
|---|---|
config/agent-prompts.php |
Agent prompt templates (Thai/English) |
flows.system_prompt column |
System prompt storage (Flow model, not Bot) |
app/Services/SecondAI/ |
AI verification pipeline |
app/Services/SecondAI/PromptInjectionDetector.php |
Injection detection (regex-based) |
app/Services/SecondAI/UnifiedCheckService.php |
Single-call unified mode |