Improvement Roadmap¶

Planned enhancements to the evaluation system and chatbot quality assurance.

Overview¶

This roadmap outlines future improvements to evaluation methodology, metrics, and tooling for the Poolula Platform chatbot.

Short Term (Weeks 1-4)¶

1. Enhanced Keyword Matching¶

Current: Simple case-insensitive substring matching

Planned: Semantic similarity scoring

Implementation:

Use sentence embeddings for keyword matching
Allow synonyms ("property" matches "real estate")
Score based on semantic distance (0.0-1.0)

Benefits:

More accurate quality scores
Reduced false negatives
Better handling of paraphrasing

2. Retrieval Metrics¶

Current: No measurement of search result quality

Planned: Precision and recall metrics for document retrieval

Metrics:

Precision = relevant_results / total_results
Recall = relevant_results / total_relevant
F1 Score = 2 × (precision × recall) / (precision + recall)

Implementation:

Add relevant_documents field to evaluation questions
Track which documents were actually retrieved
Calculate precision/recall per question

Benefits:

Verify search is finding correct documents
Detect when irrelevant documents are returned
Optimize embedding model selection

3. Response Latency Tracking¶

Current: No performance metrics

Planned: Track response time per question and category

Metrics:

Query processing time
Tool execution time
Total response time
Percentiles (p50, p90, p99)

Implementation:

{
  "question": "What is our property address?",
  "response_time_ms": 1250,
  "breakdown": {
    "query_processing": 200,
    "database_query": 50,
    "llm_generation": 1000
  }
}

Benefits:

Identify slow queries
Optimize performance bottlenecks
Set latency SLOs

Medium Term (Months 2-3)¶

4. LLM-as-Judge Scoring¶

Current: Keyword matching for response quality

Planned: Use GPT-4 to evaluate responses

Methodology:

Prompt to GPT-4:
"Evaluate this response on a scale of 0-100:

Question: {question}
Expected answer should include: {context}
Actual response: {response}

Score based on:
- Accuracy (40%)
- Completeness (30%)
- Clarity (20%)
- Relevance (10%)

Return JSON: {score: int, reasoning: str}"

Benefits:

Nuanced quality assessment
Understands paraphrasing and synonyms
Can detect hallucinations
Evaluates explanation quality

Challenges:

Cost (API calls per question)
Latency (slower than keyword matching)
Consistency (LLM outputs vary)

Mitigation:

Run LLM-as-judge weekly, not per-commit
Use GPT-4-mini for cost savings
Average multiple runs for consistency

5. User Feedback Integration¶

Current: No production feedback loop

Planned: Collect and analyze user feedback

Implementation:

Add thumbs up/down to chatbot UI
Optional comment field for poor responses
Log feedback to database

Metrics:

User Satisfaction = positive_feedback / total_feedback
Feedback Rate = total_feedback / total_queries

Dashboard:

Show satisfaction by question category
Track common complaint themes
Correlate with evaluation scores

Benefits:

Real-world quality signal
Identify gaps in golden question set
Prioritize improvements

6. Continuous Evaluation¶

Current: Manual evaluation runs

Planned: Automated nightly evaluation

Implementation:

# .github/workflows/nightly-eval.yml
name: Nightly Evaluation

on:
  schedule:
    - cron: "0 2 * * *"  # 2 AM daily

jobs:
  evaluate:
    runs-on: ubuntu-latest
    steps:
      - name: Run evaluation
        run: python scripts/evaluate_chatbot.py

      - name: Store results
        run: |
          DATE=$(date +%Y%m%d)
          cp results.json results/eval_$DATE.json

      - name: Check regression
        run: python scripts/check_regression.py

      - name: Notify on failure
        if: failure()
        run: echo "Evaluation failed!" | mail -s "Eval Alert" team@poolula.com

Benefits:

Catch regressions immediately
Track performance trends
Automated quality monitoring

Long Term (Months 4-6)¶

7. A/B Testing Framework¶

Goal: Compare different configurations systematically

Test Scenarios:

Prompt variations
Tool configurations
Embedding models
Retrieval parameters

Implementation:

class ABTestRunner:
    def run_test(self, config_a, config_b, questions):
        results_a = evaluate(config_a, questions)
        results_b = evaluate(config_b, questions)

        # Statistical significance test
        p_value = ttest(results_a, results_b)

        return {
            "config_a_score": mean(results_a),
            "config_b_score": mean(results_b),
            "winner": "A" if mean(results_a) > mean(results_b) else "B",
            "statistically_significant": p_value < 0.05
        }

Benefits:

Data-driven configuration choices
Avoid regression from "improvements"
Systematic optimization

8. Golden Question Set Expansion¶

Current: 15 questions

Target: 50+ questions with balanced coverage

Expansion Plan:

Category	Current	Target	New Questions
property_info	3	5	+2
property_financials	3	6	+3
transactions	3	8	+5
documents	2	5	+3
formation	1	3	+2
aggregations	1	5	+4
compliance	1	5	+4
hybrid	1	8	+7
governance	0	3	+3
tax	0	5	+5

Sources:

User query logs (when available)
Sample questions document (133 questions)
Stakeholder interviews
Edge cases discovered in testing

Benefits:

Better coverage of use cases
More robust quality signal
Catch edge cases

9. Multi-Model Comparison¶

Goal: Compare different LLM backends

Models to Test:

GPT-4 (current)
GPT-4-mini (cost optimization)
Claude 3.5 Sonnet (alternative)
Claude 3 Haiku (speed optimization)

Evaluation Matrix:

Model	Score	Latency	Cost/1K	Use Case
GPT-4	TBD	TBD	$0.03	Production
GPT-4-mini	TBD	TBD	$0.015	Cost-optimized
Claude Sonnet	TBD	TBD	$0.015	Alternative
Claude Haiku	TBD	TBD	$0.0008	Speed-optimized

Implementation:

Pluggable LLM backend
Run evaluation against all models
Compare quality vs cost vs speed trade-offs

10. Hallucination Detection¶

Goal: Identify when AI makes up information

Approach:

Source attribution checking
Fact verification against database
Confidence scoring

Implementation:

def detect_hallucination(response, sources):
    # Extract claims from response
    claims = extract_claims(response)

    for claim in claims:
        # Check if claim is supported by sources
        supported = verify_claim_in_sources(claim, sources)

        if not supported:
            return {
                "hallucination_detected": True,
                "unsupported_claim": claim
            }

    return {"hallucination_detected": False}

Scoring:

Penalize responses with hallucinations
Add to evaluation metrics

Future Research¶

Advanced Metrics¶

Semantic similarity scoring:

Use embedding similarity instead of keyword matching
BERT-score or similar metric

Answer equivalence:

Recognize semantically equivalent answers
"900 S 9^th St" vs "900 South Ninth Street"

Factual consistency:

Verify numerical values match database
Cross-reference dates and amounts

Evaluation UI¶

Interactive dashboard:

Visual performance trends
Drill-down by category
Compare evaluation runs
Annotate failed questions

Screenshot mockup:

┌─────────────────────────────────────────┐
│ Evaluation Dashboard                    │
├─────────────────────────────────────────┤
│ Overall Score: 87.3% ▲ +2.1%           │
│                                         │
│ [Chart: Score trend over time]          │
│                                         │
│ Category Breakdown:                     │
│ ▓▓▓▓▓▓▓▓▓▓ property_info    100%       │
│ ▓▓▓▓▓▓▓▓▓░ property_fin     93%        │
│ ▓▓▓▓▓▓░░░░ compliance       67% ⚠️      │
│                                         │
│ Failed Questions (2):                   │
│ • [compliance] LLC report deadline      │
│ • [transactions] Monthly breakdown      │
└─────────────────────────────────────────┘

Implementation Priorities¶

High Priority (Next Quarter)¶

✅ Enhanced keyword matching (semantic similarity)
✅ Retrieval metrics (precision/recall)
✅ Response latency tracking

Rationale: Low-hanging fruit, high impact on quality insight.

Medium Priority (Q2 2025)¶

✅ LLM-as-judge scoring
✅ User feedback integration
✅ Continuous evaluation (CI/CD)

Rationale: Adds production-grade quality monitoring.

Low Priority (Q3 2025+)¶

⏳ A/B testing framework
⏳ Multi-model comparison
⏳ Hallucination detection

Rationale: Advanced features for optimization and research.

Success Metrics¶

Evaluation System Goals¶

By Q2 2025:

✅ Automated daily evaluation runs
✅ User feedback integrated
✅ Latency SLOs defined and tracked
✅ Retrieval quality metrics baseline established

By Q3 2025:

✅ LLM-as-judge scoring operational
✅ 50+ golden questions
✅ A/B testing framework ready
✅ Multi-model comparison completed

Chatbot Quality Goals¶

By Q2 2025:

Overall score ≥ 90%
All categories ≥ 80%
p95 latency < 3 seconds
User satisfaction ≥ 85%

Evaluation Harness - Current system
Scoring Methodology - How scoring works
Results & Baselines - Current performance
Implementation Plan - Overall project roadmap

Improvement Roadmap¶

Overview¶

Short Term (Weeks 1-4)¶

1. Enhanced Keyword Matching¶

2. Retrieval Metrics¶

3. Response Latency Tracking¶

Medium Term (Months 2-3)¶

4. LLM-as-Judge Scoring¶

5. User Feedback Integration¶

6. Continuous Evaluation¶

Long Term (Months 4-6)¶

7. A/B Testing Framework¶

8. Golden Question Set Expansion¶

9. Multi-Model Comparison¶

10. Hallucination Detection¶

Future Research¶

Advanced Metrics¶

Evaluation UI¶

Implementation Priorities¶

High Priority (Next Quarter)¶

Medium Priority (Q2 2025)¶

Low Priority (Q3 2025+)¶

Success Metrics¶

Evaluation System Goals¶

Chatbot Quality Goals¶

Related Documentation¶