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This guide focuses on LLM distillation for production systems, with practical trade-offs for reliability, security, and cost.
You do not need frontier latency for “complete this enum in our SDK.” Distill a teacher on private examples into a student that fits Lambda memory and your p95 budget — then refuse to ship if quality floors crack. Day 43 sets budgets before training.
⚡ TL;DR: Write latency/size/quality budgets first. Generate teacher labels on curated prompts; validate; train student; gate on domain+general packs. Serve behind the Day 47 router with instant rollback to teacher.
LLM distillation: production guidance
Example: p95 ≤ 120ms for autocomplete chunk; artifact fits Lambda; quality ≥ 95% of teacher on internal SDK suite.python
for prompt in curated_prompts:
teacher_out = teacher.generate(prompt, temperature=0.2)
if validator(prompt, teacher_out):
yield {"prompt": prompt, "completion": teacher_out}
| Runtime | Student size | Notes |
|---|---|---|
| Lambda | small | cold start matters |
| ECS/GPU | medium | steadier latency |
| Edge | tiny | aggressive quant |
Production checklist
- [ ] Latency + size budgets documented
- [ ] Teacher outputs validated
- [ ] Student vs teacher delta reported
- [ ] Rollback to teacher wired
- [ ] Cost per 1k requests compared
- [ ] Safety/citation probes still pass
Series navigation
← Day 42 · Day 44 → Last updated September 11, 2026 Related: AWS Lambda Coding Agent Sandbox: Safe Tool Isolation for LLM Agents; Day 31: Lambda + Bedrock: Sync, Stream, and Batch; Bedrock Model Distillation: Shrink Coding Assistants for Edge Latency.Watch: 100 Days of AI on YouTube
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