Use hierarchical chunking with dynamic token budgeting and selective activation of retrieval‑augmented modules to keep reasoning depth while staying under 128k tokens.
1. Profile the prompt – run tiktoken.encode on the raw prompt, note token count T_raw. If T_raw > 100k, proceed to step 2.
2. Hierarchical chunking – split the input into logical sections (e.g., chapters, API specs) using nltk.sent_tokenize then group into chunks of ≈4k tokens. Store each chunk with a short meta‑summary (≈50 tokens).
3. Dynamic token budget – allocate a fixed budget B = 120k for the model. Reserve B_ctx = 30k for system + few‑shot examples, B_ret = 20k for retrieved summaries, leaving B_main = B - B_ctx - B_ret for the main context.
4. Selective retrieval – for each user query, embed the query with sentence‑transformers/all-MiniLM-L6-v2, retrieve top‑k summaries where k = floor(B_ret / 250) (≈80). Append only those summaries.
5. Prompt templating – use the vLLM flag --max-model-len 131072 and the template:
template = f"""{system_prompt}\n\n{few_shot}\n\n{retrieved_summaries}\n\n{main_context}\n\n{user_query}"""
```
6. **Evaluation loop** – after generation, compute `rougeL` and `answer correctness` on a held‑out set; if `rougeL < 0.75`, increase `k` or reduce chunk size by 10%.
7. **Cache & reuse** – store the meta‑summaries and embeddings in a Milvus collection; reuse across sessions to avoid re‑embedding.
**Technique comparison**
| Technique | Token Overhead | Reasoning Impact |
|--------------------------|----------------|------------------|
| Flat prompt (no chunk) | 0 % | high risk of truncation |
| Fixed 4k chunks | ~5 % | maintains context, modest slowdown |
| Dynamic budgeting + retrieval | ~12 % | preserves depth, best accuracy |
**vLLM config snippet**yaml
model: meta-llama/Meta-Llama-3.1-70B-Instruct
max_model_len: 131072
enable_prefix_caching: true
```