What is the role of Few-Shot Prompting when fine-tuning is not economically viable? (Part 2 Focus)

Few-shot prompting supplies task‑specific context without the cost of fine‑tuning, letting you achieve near‑state‑of‑the‑art performance within a single inference pass. **Step‑by‑step deployment** 1. **Select exemplar set** – pick 3–5 representative input‑output pairs that cover the edge cases you care about. 2. **Fit within token budget** – ensure `len(prompt) + max_output ≤ 0.75 × context_len`. For a 128k‑token model, keep the prompt ≤ 96k tokens; for 8k models, ≤ 6k. 3. **Add explicit instructions** – prepend a short system message, e.g. `You are a concise technical writer`. 4. **Configure API call** – set `temperature=0`, `top_p=1.0`, `max_tokens` to your desired output length, and enable `logprobs=5` for debugging. 5. **Iterate with validation** – run a held‑out set, compute exact match or BLEU, and adjust examples until the metric plateaus. **Quick comparison** | Approach | Cost (GPU‑hrs) | Latency per query | Typical accuracy gain | |----------|----------------|-------------------|-----------------------| | Full fine‑tune (8B) | ~120 | 150 ms (after loading) | +12 % over base | | Few‑shot (8B) | 0 | 80 ms | +9 % over base | | Zero‑shot (8B) | 0 | 70 ms | baseline | **Example OpenAI‑compatible request** ```json { "model": "gpt-4o-mini-128k", "messages": [ {"role": "system", "content": "You are a concise technical writer."}, {"role": "user", "content": "\n\n\n\n"} ], "temperature": 0, "max_tokens": 512, "logprobs": 5 } ``` Follow the checklist: ✅ exemplar relevance, ✅ token budget ≤ 75 % of context, ✅ deterministic sampling, ✅ metric‑driven iteration. This keeps the workflow cheap, reproducible, and scalable when fine‑tuning is financially prohibitive.