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Supercharge your use of large language models

Version published



0.60.0 (2024-07-07)

Renaming and making names more consistent and less disambigous

  • Rename word "promptbook"
    • Keep name "Promptbook" as name for this project.
    • Rename promptbook as pipeline of templates defined in to "pipeline"
  • Rename word "library"
    • For library used as a collection of templates use name "collection"
    • For library used as this project and package use word "package"
  • Rename methods in LlmExecutionTools
    • gptChat -> callChatModel
    • gptComplete -> callCompletionModel
  • Rename custom errors
  • Rename folder promptbook-collection -> promptbook-collection

In prepare



Promptbook logo - cube with letters P and B Promptbook

Supercharge your use of large language models

NPM Version of Promptbook logo - cube with letters P and B Promptbook Quality of package Promptbook logo - cube with letters P and B Promptbook Known Vulnerabilities Issues

📦 Package @promptbook/langtail

To install this package, run:

# Install entire promptbook ecosystem
npm i ptbk

# Install just this package to save space
npm i @promptbook/langtail

Use Langtail to ship AI apps with fewer surprises.

⚠️ Warning: Langtail integration is not finished yet. It's a placeholder for the future implementation.

This package is wrapper of Langtail's OpenAI proxy to make it easier to use inside the Promptbooks.

💙 Integration with other models

See the other models available in the Promptbook package:

Rest of the documentation is common for entire promptbook ecosystem:

🤍 The Promptbook Whitepaper

When you have a simple, single prompt for ChatGPT, GPT-4, Anthropic Claude, Google Gemini, Llama 2, or whatever, it doesn't matter how it is integrated. Whether it's the direct calling of a REST API, using the SDK, hardcoding the prompt in the source code, or importing a text file, the process remains the same.

If you need something more advanced or want to extend the capabilities of LLMs, you generally have three ways to proceed:

  1. Fine-tune the model to your specifications or even train your own.
  2. Prompt-engineer the prompt to the best shape you can achieve.
  3. Use multiple prompts in a pipeline to get the best result.

In any of these situations, but especially in (3), the Promptbook library can make your life easier and make orchestraror for your prompts.

  • Separation of concerns between prompt engineer and programmer; between code files and prompt files; and between prompts and their execution logic.
  • Set up a common format for prompts that is interchangeable between projects and language/technology stacks.
  • Preprocessing and cleaning the input data from the user.
  • Use default values - Jokers to bypass some parts of the pipeline.
  • Expect some specific output from the model.
  • Retry mismatched outputs.
  • Combine multiple models together.
  • Interactive User interaction with the model and the user.
  • Leverage external sources (like ChatGPT plugins or OpenAI's GPTs).
  • Simplify your code to be DRY and not repeat all the boilerplate code for each prompt.
  • Versioning of promptbooks
  • Reuse parts of promptbooks in/between projects.
  • Run the LLM optimally in parallel, with the best cost/quality ratio or speed/quality ratio.
  • Execution report to see what happened during the execution.
  • Logging the results of the promptbooks.
  • (Not ready yet) Caching calls to LLMs to save money and time.
  • (Not ready yet) Extend one prompt book from another one.
  • (Not ready yet) Leverage the streaming to make super cool UI/UX.
  • (Not ready yet) A/B testing to determine which prompt works best for the job.



🌍 Create website content

Instructions for creating web page content.

  • INPUT  PARAM {rawTitle} Automatically suggested a site name or empty text
  • INPUT  PARAM {rawAssigment} Automatically generated site entry from image recognition
  • OUTPUT PARAM {content} Web content
  • OUTPUT PARAM {keywords} Keywords

👤 Specifying the assigment

What is your web about?


-> {assigment} Website assignment and specification

✨ Improving the title

  • MODEL NAME gpt-4
  • POSTPROCESSING unwrapResult
As an experienced marketing specialist, you have been entrusted with improving the name of your client's business.

A suggested name from a client:

Assignment from customer:

> {assigment}

## Instructions:

-   Write only one name suggestion
-   The name will be used on the website, business cards, visuals, etc.

-> {enhancedTitle} Enhanced title

👤 Website title approval

Is the title for your website okay?


-> {title} Title for the website

🐰 Cunning subtitle

  • MODEL NAME gpt-4
  • POSTPROCESSING unwrapResult
As an experienced copywriter, you have been entrusted with creating a claim for the "{title}" web page.

A website assignment from a customer:

> {assigment}

## Instructions:

-   Write only one name suggestion
-   Claim will be used on website, business cards, visuals, etc.
-   Claim should be punchy, funny, original

-> {claim} Claim for the web

🚦 Keyword analysis

  • MODEL NAME gpt-4
As an experienced SEO specialist, you have been entrusted with creating keywords for the website "{title}".

Website assignment from the customer:

> {assigment}

## Instructions:

-   Write a list of keywords
-   Keywords are in basic form

## Example:

-   Ice cream
-   Olomouc
-   Quality
-   Family
-   Tradition
-   Italy
-   Craft

-> {keywords} Keywords

🔗 Combine the beginning


# {title}

> {claim}

-> {contentBeginning} Beginning of web content

🖋 Write the content

  • MODEL VARIANT Completion
  • MODEL NAME gpt-3.5-turbo-instruct
As an experienced copywriter and web designer, you have been entrusted with creating text for a new website {title}.

A website assignment from a customer:

> {assigment}

## Instructions:

-   Text formatting is in Markdown
-   Be concise and to the point
-   Use keywords, but they should be naturally in the text
-   This is the complete content of the page, so don't forget all the important information and elements the page should contain
-   Use headings, bullets, text formatting

## Keywords:


## Web Content:


-> {contentBody} Middle of the web content

🔗 Combine the content



-> {content}

Following is the scheme how the promptbook above is executed:

%% 🔮 Tip: Open this on GitHub or in the VSCode website to see the Mermaid graph visually

flowchart LR
  subgraph "🌍 Create website content"

      direction TB

      templateSpecifyingTheAssigment(👤 Specifying the assigment)
      templateImprovingTheTitle(✨ Improving the title)
      templateWebsiteTitleApproval(👤 Website title approval)
      templateCunningSubtitle(🐰 Cunning subtitle)
      templateKeywordAnalysis(🚦 Keyword analysis)
      templateCombineTheBeginning(🔗 Combine the beginning)
      templateWriteTheContent(🖋 Write the content)
      templateCombineTheContent(🔗 Combine the content)


      classDef input color: grey;
      classDef output color: grey;


More template samples

Note: We are using postprocessing functions like unwrapResult that can be used to postprocess the result.

📦 Packages

This library is divided into several packages, all are published from single monorepo. You can install all of them at once:

npm i ptbk

Or you can install them separately:

⭐ Marked packages are worth to try first

📚 Dictionary

The following glossary is used to clarify certain basic concepts:


Prompt in a text along with model requirements, but without any execution or templating logic.

For example:

    "request": "Which sound does a cat make?",
    "modelRequirements": {
        "variant": "CHAT"
    "request": "I am a cat.\nI like to eat fish.\nI like to sleep.\nI like to play with a ball.\nI l",
    "modelRequirements": {
        "variant": "COMPLETION"

Prompt Template

Similar concept to Prompt, but with templating logic.

For example:

    "request": "Which sound does a {animalName} make?",
    "modelRequirements": {
        "variant": "CHAT"

Model Requirements

Abstract way to specify the LLM. It does not specify the LLM with concrete version itself, only the requirements for the LLM. NOT chatgpt-3.5-turbo BUT CHAT variant of GPT-3.5.

For example:

    "variant": "CHAT",
    "version": "GPT-3.5",
    "temperature": 0.7

Execution type

Each block of promptbook can have a different execution type. It is specified in list of requirements for the block. By default, it is Prompt template

  • (default) Prompt template The block is a prompt template and is executed by LLM (OpenAI, Azure,...)
  • SIMPLE TEMPLATE The block is a simple text template which is just filled with parameters
  • Script The block is a script that is executed by some script runtime, the runtime is determined by block type, currently only javascript is supported but we plan to add python and typescript in the future.
  • PROMPT DIALOG Ask user for input


Parameters that are placed in the prompt template and replaced to create the prompt. It is a simple key-value object.

    "animalName": "cat",
    "animalSound": "Meow!"

There are three types of template parameters, depending on how they are used in the promptbook:

  • INPUT PARAMETERs are required to execute the promptbook.
  • Intermediate parameters are used internally in the promptbook.
  • OUTPUT PARAMETERs are explicitelly marked and they are returned as the result of the promptbook execution.

Note: Parameter can be both intermedite and output at the same time.


Promptbook is core concept of this library. It represents a series of prompt templates chained together to form a pipeline / one big prompt template with input and result parameters.

Internally it can have multiple formats:

  • file in custom markdown format described above
  • (concept) .ptbk format, custom fileextension based on markdown
  • (internal) JSON format, parsed from the file

Promptbook Library

Library of all promptbooks used in your application. Each promptbook is a separate file with unique PIPELINE URL. Theese urls are used to reference promptbooks in other promptbooks or in the application code.

Prompt Result

Prompt result is the simplest concept of execution. It is the result of executing one prompt (NOT a template).

For example:

    "response": "Meow!",
    "model": "chatgpt-3.5-turbo"

Execution Tools

ExecutionTools is an interface which contains all the tools needed to execute prompts. It contais 3 subtools:

  • LlmExecutionTools
  • ScriptExecutionTools
  • UserInterfaceTools

Which are described below:

LLM Execution Tools

LlmExecutionTools is a container for all the tools needed to execute prompts to large language models like GPT-4. On its interface it exposes common methods for prompt execution. Internally it calls OpenAI, Azure, GPU, proxy, cache, logging,...

LlmExecutionTools an abstract interface that is implemented by concrete execution tools:

  • OpenAiExecutionTools
  • AnthropicClaudeExecutionTools
  • AzureOpenAiExecutionTools
  • LangtailExecutionTools
  • (Not implemented yet) BardExecutionTools
  • (Not implemented yet) LamaExecutionTools
  • (Not implemented yet) GpuExecutionTools
  • And a special case are MultipleLlmExecutionTools that combines multiple execution tools together and tries to execute the prompt on the best one.
  • Another special case are RemoteLlmExecutionTools that connect to a remote server and run one of the above execution tools on that server.
  • The another special case is MockedEchoLlmExecutionTools that is used for testing and mocking.
  • The another special case is LogLlmExecutionToolsWrapper that is technically also an execution tools but it is more proxy wrapper around other execution tools that logs all calls to execution tools.
Script Execution Tools

ScriptExecutionTools is an abstract container that represents all the tools needed to EXECUTE SCRIPTs. It is implemented by concrete execution tools:

  • JavascriptExecutionTools is a wrapper around vm2 module that executes javascript code in a sandbox.
  • JavascriptEvalExecutionTools is wrapper around eval function that executes javascript. It is used for testing and mocking NOT intended to use in the production due to its unsafe nature, use JavascriptExecutionTools instead.
  • (Not implemented yet) TypescriptExecutionTools executes typescript code in a sandbox.
  • (Not implemented yet) PythonExecutionTools executes python code in a sandbox.

There are postprocessing functions that can be used to postprocess the result.

User Interface Tools

UserInterfaceTools is an abstract container that represents all the tools needed to interact with the user. It is implemented by concrete execution tools:

  • (Not implemented yet) ConsoleInterfaceTools is a wrapper around readline module that interacts with the user via console.
  • SimplePromptInterfaceTools is a wrapper around window.prompt synchronous function that interacts with the user via browser prompt. It is used for testing and mocking NOT intended to use in the production due to its synchronous nature.
  • CallbackInterfaceTools delagates the user interaction to a async callback function. You need to provide your own implementation of this callback function and its bind to UI.


Executor is a simple async function that takes input parameters and returns output parameters. It is constructed by combining execution tools and promptbook to execute together.

🃏 Jokers (conditions)

Joker is a previously defined parameter that is used to bypass some parts of the pipeline. If the joker is present in the template, it is checked to see if it meets the requirements (without postprocessing), and if so, it is used instead of executing that prompt template. There can be multiple wildcards in a prompt template, if so they are checked in order and the first one that meets the requirements is used.

If none of the jokers meet the requirements, the prompt template is executed as usual.

This can be useful, for example, if you want to use some predefined data, or if you want to use some data from the user, but you are not sure if it is suitable form.

When using wildcards, you must have at least one minimum expectation. If you do not have a minimum expectation, the joker will always fulfil the expectation because it has none, so it makes no logical sense.

Look at sample.

Postprocessing functions

You can define postprocessing functions when creating JavascriptEvalExecutionTools:

Additionally there are some usefull string-manipulation build-in functions, which are listed here.


Expect command describes the desired output of the prompt template (after post-processing) It can set limits for the maximum/minimum length of the output, measured in characters, words, sentences, paragraphs,...

Note: LLMs work with tokens, not characters, but in Promptbooks we want to use some human-recognisable and cross-model interoperable units.

# ✨ Sample: Expectations

-   INPUT  PARAMETER {yourName} Name of the hero

## 💬 Question



There are two types of expectations which are not strictly symmetrical:

Minimal expectations
  • EXPECT MIN 0 ... is not valid minimal expectation. It makes no sense.
  • EXPECT JSON is both minimal and maximal expectation
  • When you are using JOKER in same prompt template, you need to have at least one minimal expectation
Maximal expectations
  • EXPECT MAX 0 ... is valid maximal expectation. For example, you can expect 0 pages and 2 sentences.
  • EXPECT JSON is both minimal and maximal expectation

Look at and samples for more.

Execution report

Execution report is a simple object or markdown that contains information about the execution of the pipeline.

See the example of such a report

Remote server

Remote server is a proxy server that uses its execution tools internally and exposes the executor interface externally.

You can simply use RemoteExecutionTools on client-side javascript and connect to your remote server. This is useful to make all logic on browser side but not expose your API keys or no need to use customer's GPU.

👨‍💻 Usage and integration (for developers)

🔌 Usage in Typescript / Javascript

➕➖ When to use Promptbook?

➕ When to use

  • When you are writing app that generates complex things via LLM - like websites, articles, presentations, code, stories, songs,...
  • When you want to separate code from text prompts
  • When you want to describe complex prompt pipelines and don't want to do it in the code
  • When you want to orchestrate multiple prompts together
  • When you want to reuse parts of prompts in multiple places
  • When you want to version your prompts and test multiple versions
  • When you want to log the execution of prompts and backtrace the issues

➖ When not to use

  • When you are writing just a simple chatbot without any extra logic, just system messages

🐜 Known issues

🧼 Intentionally not implemented features


If you have a question start a discussion, open an issue or write me an email.

Why not just use the OpenAI SDK / Anthropic Claude SDK / ...?

Different levels of abstraction. OpenAI library is for direct use of OpenAI API. This library is for a higher level of abstraction. It is for creating prompt templates and promptbooks that are independent of the underlying library, LLM model, or even LLM provider.

How is it different from the Langchain library?

Langchain is primarily aimed at ML developers working in Python. This library is for developers working in javascript/typescript and creating applications for end users.

We are considering creating a bridge/converter between these two libraries.

Promptbooks vs. OpenAI`s GPTs

GPTs are chat assistants that can be assigned to specific tasks and materials. But they are still chat assistants. Promptbooks are a way to orchestrate many more predefined tasks to have much tighter control over the process. Promptbooks are not a good technology for creating human-like chatbots, GPTs are not a good technology for creating outputs with specific requirements.

Where should I store my promptbooks?

If you use raw SDKs, you just put prompts in the sourcecode, mixed in with typescript, javascript, python or whatever programming language you use.

If you use promptbooks, you can store them in several places, each with its own advantages and disadvantages:

  1. As source code, typically git-committed. In this case you can use the versioning system and the promptbooks will be tightly coupled with the version of the application. You still get the power of promptbooks, as you separate the concerns of the prompt-engineer and the programmer.

  2. As data in a database In this case, promptbooks are like posts / articles on the blog. They can be modified independently of the application. You don't need to redeploy the application to change the promptbooks. You can have multiple versions of promptbooks for each user. You can have a web interface for non-programmers to create and modify promptbooks. But you lose the versioning system and you still have to consider the interface between the promptbooks and the application (= input and output parameters).

  3. In a configuration in environment variables. This is a good way to store promptbooks if you have an application with multiple deployments and you want to have different but simple promptbooks for each deployment and you don't need to change them often.

What should I do when I need same promptbook in multiple human languages?

A single promptbook can be written for several (human) languages at once. However, we recommend that you have separate promptbooks for each language.

In large language models, you will get better results if you have prompts in the same language as the user input.

The best way to manage this is to have suffixed promptbooks like and for each supported language.

⌚ Changelog


📜 License

Promptbook by Pavol Hejný is licensed under CC BY 4.0

🎯 Todos


🖋️ Contributing

I am open to pull requests, feedback, and suggestions. Or if you like this utility, you can ☕ buy me a coffee or donate via cryptocurrencies.

You can also ⭐ star the promptbook package, follow me on GitHub or various other social networks.



Last updated on 15 Jul 2024

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