> For the complete documentation index, see [llms.txt](https://docs.promptspace.app/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.promptspace.app/psl-docs/chaining-sections.md).

# Chaining Sections

Using response from a prompt in a subsequent prompt.

Prompts are specified in the sections of the form `[prompt.<prompt_section>]`. Responses from previous prompts can be used as arguments in subsequent prompts by using the format `{{response.prompt_name}}`.&#x20;

#### Example 1

```ini
[ask.category]
description = Please provide a category from which you would like the AI to choose a random personality from. For example: US Basketball players

; Use priming to make GPT output personality info as a valid INI
; User input `category` is used in the message to GPT which is an example of chaining.
[prompt.get_personality]
model_name = gpt-3.5-turbo
priming.0.user = Our users want to play taboo against wikipedia articles. They will have five questions to guess the personality from their Wikipedia articles.
    You will be provided with a category. Please provide a personality for that category. Make it interesting. For example, Lionel Messy for soccer is not very challenging got users.
    Provide your answer in the following `ini` format which has exactly three fields `name`, `reason`, `hint`. The reason should not be more than a couple of sentences, and hint should not be spoiler.
    [personality]
    name = <name>
    reason = <reason for picking the personality with the category as context>
    hint = <a hint to help the user guess the personality without spoiling the game>

    Say okay to continue
priming.0.assistant = Okay.
priming.1.user =
    category: historical records
priming.1.assistant = [personality]
    name = Tsutomu Yamaguchi
    reason = Tsutomu Yamaguchi is an interesting personality in the "historical records" category. He is known for surviving both the Hiroshima and Nagasaki atomic bombings during World War II, making him a unique historical figure.
    hint = The personality also fits in the following categories: "World War II", "Japanese personalities"
message =
    category: {{input.category}}
output_type = ini
display = False

; Use the personality name to fetch their wiki article for QnA.
; This using of previous response is chaining.
[prompt.chain]
model_name = langchain_document_loader
document_type = wikipedia
chain_type = ConversationalRetrievalChain
query = {{response.get_personality.personality.name}}
display = False
```

Here the response of the first prompt section `[prompt.personality]` is used in prompt section `[prompt.chain]` in the query attribute that our `langchain_document_loader` model supports.\
\
Note here also that `response.personality` is not simply a string but an objectified version of `ini` (output type for the previous font is `ini`).&#x20;

#### Example 2

Continuing the previous example, one can use response from `[prompt.chain]` in a subsequent prompt like this:

```ini
[ask.guess_1]
description = Please provide your first question (or guess) for the personality.

[prompt.guess_1]
model_name = identity
message = {{response.chain.ask(input.guess_1)}}
display = False
```