Claude + Groq Hybrid LLM — AI University Memory Agent
After each learning session in AI University, a Memory Agent automatically builds a structured learner profile — weak providers, strong providers, preferred learning style. Next session, quizzes are personalized based on that profile.
The trick: two models, two jobs — Claude Sonnet for deep profile extraction, Groq Llama for real-time quiz scoring.
Architecture
CODE
Session ends
→ learner.update_profile (Edge Function)
→ Claude Sonnet → structured JSON profile
→ UPSERT into ai_university_learner_profiles
Quiz answer submitted
→ quiz.evaluate (Edge Function)
→ Groq Llama 3.3 70B → JSON score {result, confidence}
→ fallback: string match
Memory Agent — Claude Extracts the Profile
CODE
// supabase/functions/ai-hub — learner.update_profile
const prompt = `Extract a structured learner profile from this session data.
Session summary: ${sessionSummary}
Score data: ${JSON.stringify(scores).slice(0, 2000)}
Return JSON: {"weak_providers":["..."],"strong_providers":["..."],"preferred_style":"visual|text|voice","insights":"..."}`;
const claudeResp = await fetch("https://api.anthropic.com/v1/messages", {
body: JSON.stringify({
model: "claude-sonnet-4-6",
max_tokens: 512,
messages: [{ role: "user", content: prompt }],
}),
});
// Strip code fences before parsing
const rawText = claudeData.content[0].text;
const profile = JSON.parse(rawText.replace(/```
{% endraw %}
json\n?|\n?
{% raw %}
```/g, "").trim());
Save to Supabase:
CODE
await admin.from("ai_university_learner_profiles").upsert({
user_id,
weak_providers: profile.weak_providers ?? [],
strong_providers: profile.strong_providers ?? [],
preferred_style: profile.preferred_style ?? "text",
profile_json: profile,
total_sessions: (existing?.total_sessions ?? 0) + 1,
}, { onConflict: "user_id" });
Quiz Evaluator — Groq Scores Answers Fast
CODE
// quiz.evaluate — Groq Llama 3.3 70B, JSON mode
const groqResp = await fetch("https://api.groq.com/openai/v1/chat/completions", {
body: JSON.stringify({
model: "llama-3.3-70b-versatile",
max_tokens: 100,
temperature: 0,
response_format: { type: "json_object" }, // guaranteed JSON
messages: [{
role: "user",
content: `Question: ${question}\nExpected: ${correctAnswer}\nUser: ${userAnswer}
Score: {"result":"correct|incorrect|partial","confidence":0-100}`,
}],
}),
}).catch(() => null);
// Groq failure → fallback to exact string match
if (!groqResp || !groqResp.ok) {
const match = userAnswer.trim().toLowerCase() === correctAnswer.trim().toLowerCase();
return json({ result: match ? "correct" : "incorrect", confidence: 100, fallback: true });
}
Why Two Models?
| Task | Model | Reason |
|---|---|---|
| Learner profile extraction | Claude Sonnet 4.6 | Complex reasoning, structured JSON quality |
| Quiz scoring | Groq Llama 3.3 70B | Low latency, high volume, free tier |
Claude runs once at session end. Groq runs on every quiz answer. Matching the model to the task cuts costs without sacrificing quality.
DB Schema
CODE
CREATE TABLE ai_university_learner_profiles (
user_id uuid PRIMARY KEY REFERENCES auth.users,
weak_providers text[] DEFAULT '{}',
strong_providers text[] DEFAULT '{}',
preferred_style text DEFAULT 'text',
profile_json jsonb DEFAULT '{}',
total_sessions int DEFAULT 0,
updated_at timestamptz DEFAULT now()
);
Key Takeaways
Assign models by strength — Claude for deep analysis, Groq for speed
response_format: json_object— eliminates JSON parse errors from Groq
Strip code fences — Claude wraps JSON injson→ strip beforeJSON.parse
Always fallback — Groq outage shouldn't break quiz scoring
Building in public: https://my-web-app-b67f4.web.app/
FlutterWeb #Supabase #buildinpublic #LLM #FlutterTips
Vollständiger Original-Artikel
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