Performance Evaluate Questions

Performance Evaluate Questions

Uses LLM to generate summaries for quarterly connection questions based on evidence.

Inputs

Workflow

1. Get Quarter

• Current year, quarter (e.g., Q1 2026)

2. Load Questions & Evidence

• Read questions.json — questions, custom_questions
• Filter by question_id if provided
• Load daily events from daily/*.json for evidence lookup
• Load summary.json for competency scores

3. Build Evaluation Data

• For each question: auto_evidence IDs → fetch events
• Build: id, text, subtext, evidence_count, evidence_events, manual_notes

4. Build Prompts

• For each question: "You are helping prepare quarterly performance review."
• QUESTION, EVIDENCE, MANUAL NOTES, COMPETENCY SCORES
• "Write 2-3 paragraphs, first person, highlight accomplishments, specific examples"

5. Generate Summaries

• Call LLM (ollama_generate or claude) for each prompt
• Map responses to question IDs

6. Save

• Update questions in questions.json with llm_summary, last_evaluated

7. Log

• memory_session_log("Evaluated {saved} quarterly questions with AI", "Quarter: ...")

Output

Summary: quarter, questions evaluated, total. Per-question: evidence items, status.