Core Loop

Context → Preferences → Research → Match → Recommend

Every recommendation requires: knowing the user + knowing the options.

Check sources.md for where to find user context. Check categories.md for domain-specific factors.

Step 1: Context Gathering

Before recommending, search user context. See sources.md for full source list.

Minimum output: 3-5 relevant user signals before proceeding. If insufficient, ask targeted questions.

Step 2: Preference Extraction

From gathered context, extract:

Output: 3-5 bullet preference profile for this request.

Step 3: Research Options

Now—and only now—research candidates:

• Breadth first: Don't anchor on first good option
• Source quality: Prioritize reviews, ratings, expert opinions
• Recency: Check if information is current
• Availability: Confirm options are actually accessible

Output: Shortlist of 3-7 viable candidates with key attributes.

Step 4: Match & Rank

Score each candidate against the preference profile:

Candidate → Values alignment + Constraint fit + History match + Mood fit

Disqualify anything that violates hard constraints.

Rank by total alignment, not just one dimension.

Step 5: Recommend

Present 1-3 recommendations:

🎯 RECOMMENDATION: [Option]
📌 WHY: Matches [preference], avoids [constraint]
⚖️ TRADEOFF: Less [X] than [Alternative]
🔍 CONFIDENCE: [Level] — based on [data quality]

Adaptive Learning

After each recommendation:

• Track outcome: Accepted? Modified? Rejected?
• Update preferences: Acceptance = reinforcement, rejection = adjustment
• Note exceptions: "Normally X, but for Y context preferred Z"

Store learnings in memory for future recommendations.

Traps

• Projecting — Your taste ≠ their taste
• Recency bias — Last choice isn't always preference
• Ignoring context — Tuesday lunch ≠ anniversary dinner
• Over-filtering — Too many constraints = nothing fits
• Stale data — Preferences evolve, verify periodically

Recommendations are predictions. More context = better predictions.