Tier C: Advanced Features (Caching, Tool-Use, Feedback Loop)

[BPMSoftwareSolutions]

Overview

Implement advanced features to further improve quality, speed, and user experience.

Problem

After Tier A + B, remaining improvements require:

• Faster response for repeated jobs (caching)
• Structured outputs via tool-use / function calling
• Continuous improvement via user feedback
• Advanced prompt optimization

Solution

Implement 3 advanced features:

1. Prompt Caching: Cache repeated job descriptions
2. Tool-Use: Use function calling for structured transforms
3. Feedback Loop: Collect user ratings and improve model

Deliverables

1. Prompt Caching

• Goal: Reduce latency for repeated jobs
• Implementation:
  • Cache job descriptions + retrieved context
  • Use hash of job text as cache key
  • TTL: 24 hours
  • Storage: Redis or in-memory
• Expected Speedup: 3-5x faster for cached jobs
• Files:
  • n8n/scripts/cache_manager.py - Cache logic
  • n8n/n8n/workflows/tailor.json - Add caching node

2. Tool-Use / Function Calling

• Goal: Structured outputs via function calling
• Implementation:
  • Define functions for resume operations:
    • format_bullet(text, metrics, action_verb)
    • select_top_skills(skills, count)
    • generate_summary(bullets, role)
  • Use Ollama function calling (if supported)
  • Fallback to structured prompting
• Benefits:
  • Guaranteed valid JSON
  • Better control over output format
  • Easier to validate and transform
• Files:
  • n8n/scripts/function_definitions.py - Function specs
  • n8n/n8n/workflows/tailor.json - Add tool-use node

3. Feedback Loop

• Goal: Continuous improvement via user feedback
• Implementation:
  • Add rating UI to resume artifacts
  • Collect ratings (1-5 stars)
  • Store feedback in data/feedback.jsonl
  • Analyze feedback to identify patterns
  • Use feedback to improve prompts/training
• Metrics:
  • Average rating over time
  • Rating distribution
  • Common issues from low ratings
  • Improvement trends
• Files:
  • n8n/scripts/feedback_analyzer.py - Analyze feedback
  • web/components/RatingWidget.tsx - Rating UI
  • n8n/n8n/workflows/collect_feedback.json - Feedback workflow

4. Advanced Prompt Optimization

• Goal: Optimize prompts based on feedback
• Implementation:
  • A/B test different prompt variations
  • Track performance of each variation
  • Automatically select best performing prompt
  • Gradually roll out improvements
• Files:
  • n8n/scripts/prompt_optimizer.py - Optimization logic
  • n8n/prompts/ - Prompt variations

5. Documentation

• File: docs/TIER_C_ADVANCED_FEATURES.md
• Contents:
  • Caching architecture
  • Tool-use implementation
  • Feedback loop design
  • Prompt optimization process
  • Deployment guide

Success Criteria

• ✅ Prompt caching implemented and working
• ✅ Cached jobs respond 3-5x faster
• ✅ Tool-use / function calling implemented
• ✅ 100% valid JSON from function calling
• ✅ Feedback loop collecting ratings
• ✅ Feedback analysis dashboard
• ✅ Prompt optimization improving ratings
• ✅ Documentation complete

Demonstrable Improvements

1. Speed: 3-5x faster for repeated jobs (via caching)
2. Reliability: 100% valid JSON (via tool-use)
3. Quality: Continuous improvement (via feedback loop)
4. User Experience: Rating UI for feedback
5. Insights: Analytics dashboard for feedback trends

Implementation Guide

See docs/TIER_C_ADVANCED_FEATURES.md for detailed instructions.

Estimated Effort

• Time: 4-6 weeks
• Difficulty: High
• Dependencies: Tier A + B complete
• Skills: Backend (caching), LLM (tool-use), Analytics (feedback)

Files to Create

• n8n/scripts/cache_manager.py - Caching logic
• n8n/scripts/function_definitions.py - Function specs
• n8n/scripts/feedback_analyzer.py - Feedback analysis
• n8n/scripts/prompt_optimizer.py - Prompt optimization
• web/components/RatingWidget.tsx - Rating UI
• n8n/n8n/workflows/collect_feedback.json - Feedback workflow
• docs/TIER_C_ADVANCED_FEATURES.md - Documentation

Files to Modify

• n8n/n8n/workflows/tailor.json - Add caching + tool-use
• web/pages/resume.tsx - Add rating widget

Related Issues

• Parent: Enhance Resume Tailoring System: Connect n8n to FAISS + Schema Enforcement #60 (Main enhancement issue)
• Previous: Tier B: LoRA Fine-tuning for Llama 3.1 #64 (Tier B - LoRA Fine-tuning)

Acceptance Criteria

• Prompt caching implemented
• Tool-use / function calling implemented
• Feedback loop collecting ratings
• Feedback analysis dashboard
• Prompt optimization improving ratings
• All success criteria met
• Documentation complete
• Code reviewed and merged

Notes

• This is optional and can be done after Tier A + B
• Can be implemented incrementally (caching first, then tool-use, then feedback)
• Feedback loop requires user interaction (ratings)
• Prompt optimization requires statistical analysis

Future Enhancements

• Multi-armed bandit for prompt selection
• Reinforcement learning from feedback
• Automated prompt generation
• A/B testing framework
• Real-time analytics dashboard

Labels

• enhancement
• rag
• n8n
• tier-c
• advanced
• optional
• future