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A playbook for effectively prompting post-trained LLMs
| Date | Stars |
|---|---|
| 2026-07-31 | 900 |
| 2026-08-06 | 900 |
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# LLM Prompt Tuning Playbook
**Varun Godbole, Ellie Pavlick**
## Table of Contents
- [Who is this document for?](#who-is-this-document-for)
- [Why a tuning playbook?](#why-a-tuning-playbook)
- [Background: Pre-training vs. Post-training](#background-pre-training-vs-post-training)
- [Pre-training](#pre-training)
- [The "Cinematic Universe" Intuition of Pre-training](#the-cinematic-universe-intuition-of-pre-training)
- [Post-training](#post-training)
- [Post-training Data Collection](#post-training-data-collection)
- [Considerations for Prompting](#considerations-for-prompting)
- [A rudimentary "style guide" for prompts](#a-rudimentary-style-guide-for-prompts)
- [Procedure for iterating on new system instructions](#procedure-for-iterating-on-new-system-instructions)
- [Some thoughts on when LLMs are useful](#some-thoughts-on-when-llms-are-useful)
- [More Resources](#more-resources)
- [Acknowledgements](#acknowledgements)
## Who is this document for?
This document is for anyone who would like to get better at prompting post-trained LLMs. We assume that readers have had some basic interactions with some sort of LLM (e.g. Gemini), but we do not assume a rigorous technical understanding.
The first half of the document provides mental models on the nature of post-training and prompting. The second half of this document provides more concrete prescriptions and a high-level procedure for tuning prompts. Given the pace of innovation with LLMs, we suspect that the second half is likely to go stale a lot faster than the first half.
## Why a tuning playbook?
This playbook was inspired by the [Deep Learning Tuning Playbook](https://github.com/google-research/tuning_playbook), a guide for tuning hyperparameters for deep learning workloads.
The “art” of prompting, much like the broader field of deep learning, is empirical at best and alchemical at worst. While LLMs are rapidly transforming numerous applications, effective prompting strategies remain an open question for the field. This document was born out of a few years of working with LLMs, and countless requests for prompt engineering assistance. It represents an attempt to consolidate and share both helpful intuitions and practical prompting techniques.
We are a pair of researchers and engineers that have worked with LLMs for a few years. Having said that, this document shouldn’t be viewed as a definitive truth nor should it be viewed as the collective position of the Gemini post-training team. Rather, it’s a collection of our personal observations and best practices. We hope that this playbook will act as a snapshot of our current thinking, which might get updated in the future on a best effort basis as our beliefs change and as new knowledge becomes available.
We hope that by writing down our concrete set of mental models and processes, the community can work together to find better and more systematic prompting strategies.
This playbook is exclusively focused on the various post-trained versions of Gemini. Anecdotally, some of the prescriptions in this document might generalize to other models. But we have less experience with them.
## Background: Pre-training vs. Post-training
### Pre-training
“Pre-training” is an old concept from deep learning. Essentially:
1. You have a small dataset that you actually care about (i.e. Dataset A), and a large Dataset B that isn’t actually A, but similar in at least some important aspects. For example, A could involve a small amount of mammography images and B could be a large academic dataset of natural images like ImageNet.
2. You train a model on the large Dataset B with the hope that it will learn some generally useful features. You then “fine-tune” it on Dataset A to get better performance on A’s validation set than if you trained the model directly from scratch on A. That is, you simply continue training on Dataset A using the same training procedure that you had used on Dataset B. This way, by the time your moExcerpt of 42,082 characters
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matched fp:2f1dab0c553485aa, llm:description: 'A playbook for effectively prompting post-trained LLMs' (prompting guide for LLMs)