Algernon Synthesis

From the tags and topics of reviewed cards across all studied materials, identify 3-5 concept pairs that appear in multiple materials but carry a different meaning or role in each context.

Examples of strong synthesis pairs:

• "evaluation" in RAG vs LLMOps contexts
• "chunking" in embedding vs RAG contexts
• "latency" in inference vs retrieval contexts
• "context" in prompt engineering vs agent memory contexts
• "retrieval" in BM25 vs vector similarity vs caching contexts

Prefer pairs where the same word genuinely means something different — that contrast is the richest learning opportunity.

Step 3 — Synthesis Questions

For each concept pair, ask:

AskUserQuestion (free text):

"[CONCEPT] appears in both [MATERIAL_A] and [MATERIAL_B]. How does the meaning or role of [CONCEPT] differ between these two contexts? Where do they overlap?"

After each answer, give brief feedback:

• Name what the user connected well.
• Name any distinction they missed (one sentence only — do not lecture).

Step 4 — Production Scenario Challenge

AskUserQuestion (free text):

"If you were building a production AI system, how would the knowledge from [MATERIAL_A] and [MATERIAL_B] work together? Give a concrete scenario with specific design decisions."

Evaluate for:

1. Coherence — does the scenario make technical sense?
2. Specificity — are there real design decisions, not just buzzwords?
3. Correct use — are terms from both materials used accurately?

Step 5 — Summary

Synthesis session complete.
Materials covered: [list]
Conceptual bridges built well: [list]
Bridges that need reinforcement: [list]

Save to Notion (optional)

If $NOTION_CLI is available and $NOTION_PAGE_ID is set:

"$NOTION_CLI" append --page-id "$NOTION_PAGE_ID" --content "MARKDOWN"

Save Memory

echo "[HH:MM] synthesis session | Materials: LIST | Needs reinforcement: LIST" \
  >> "${ALGERNON_HOME}/memory/conversations/YYYY-MM-DD.md"