Tutorials Prompt Engineering Tutorial
AI Throughput Optimization — Complete Guide
AI Throughput Optimization — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 85 of 100
AI Throughput Optimization
Prompts ✓ → Apps
Apps · 2 — RAG & agents · ~10 min · Module 9: Performance & Optimization
What is this?
Throughput optimization increases completed requests per second via batching, async workers, and parallel independent LLM calls.
Why should you care?
PromptVerse ingest embeds 100 chunks per API batch; classify workers run 50 concurrent with rate-limit aware backoff.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
embed_batch = chunks.slice(i, i+100)
await openai.embeddings.create({ input: embed_batch.map(c=>c.text) })
// classify pool: p-limit(50) with 429 exponential backoffWhat happened?
- Batch embeddings amortize HTTP overhead.
- Concurrency pool with backoff maximizes throughput without ban.
Practice next
- Batch 32 texts in one embed call.
- Compare time vs 32 singles.
- Add concurrency limit 10.
- Pipeline classify parallel map steps.
- Auto-scale workers on queue depth.
Remember
Batch embeddings. Bounded concurrency + backoff. Separate ingest from query workers.
Re-index weekend
1M chunks must embed overnight.
Outcome: Batch+32 workers finish in 6 hours.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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