How to Impress Interviewers with Smart Token‑Optimization Strategies for LLMs

Background

When interviewers ask how to reduce token consumption when calling large models, suggesting cheaper models is insufficient.

Key Points

Token cost originates from prompt input, context, and model output. Optimize it through five strategies:

Simplify Input : Use structured prompt templates (role + task + format + constraints), keep sentences concise, remove redundant words, and apply Retrieval‑Augmented Generation (RAG) to split long documents and feed only high‑relevance chunks.

Context Management : As dialogue rounds increase, context grows. Techniques include:

Fixed‑window truncation – retain only the most recent three to five turns.

Conversation summarization – after about ten turns, invoke a lightweight model to compress history into a short summary.

Timeout clearing – reset context after a period of inactivity.

Control Output : Limit response length with the max_tokens parameter, e.g., max_tokens=1000, to prevent unnecessary token generation.

Model Tiering : Assign simple tasks (e.g., copy‑editing, keyword extraction) to free or lightweight models such as Zhipu GLM‑4‑Flash or Tongyi Qianwen Lite, while routing complex tasks (deep reasoning, multimodal generation, professional writing) to full‑size models like GPT‑4o or Claude 3.5 Sonnet.

Cache Reuse : Store results of high‑frequency, deterministic queries in Redis and return cached answers instead of invoking the model again (e.g., account‑binding steps, refund procedures).

Conclusion

Applying these five measures—structured prompts with RAG, fixed‑window or summarised context, token‑limited outputs, hierarchical model selection, and Redis caching—significantly lowers token expenses while maintaining answer quality, leaving a strong impression on interviewers.