Principle:Deepset ai Haystack Prompt Templating
Overview
Prompt templating dynamically constructs LLM prompts from templates with variable substitution, enabling reusable prompt patterns across queries and documents. By separating prompt structure from dynamic content, developers can maintain consistent prompt designs while injecting different queries, documents, and context at runtime.
Domains
- NLP
- Prompt_Engineering
Theory
Prompt templating employs template-based prompt construction using Jinja2 syntax for variable interpolation. The core idea is the separation of concerns between the static prompt structure (instructions, formatting, few-shot examples) and the dynamic content (user queries, retrieved documents, contextual variables).
Jinja2 Template Syntax
Jinja2 provides a powerful templating language with the following key features relevant to prompt construction:
- Variable interpolation:
{{ variable_name }}inserts the value of a variable into the template at render time. - Control structures:
{% for item in collection %}and{% if condition %}allow looping and conditional logic within templates. - Default values:
{{ variable | default('fallback') }}provides fallback values when variables are not supplied. - Filters: Jinja2 filters transform variable values during rendering (e.g.,
upper,trim).
Template Variable Categories
Prompt templates typically incorporate several categories of variables:
- Query variables: The user's question or input text.
- Document variables: Retrieved documents or context passages, often iterated over with loop constructs.
- Instruction variables: Dynamic instructions such as target language, output format, or persona definitions.
- Metadata variables: Document metadata fields (titles, source names, timestamps) used for structured prompt formatting.
Sandboxed Execution
For security, template rendering occurs within a sandboxed Jinja2 environment (SandboxedEnvironment). This prevents template code from accessing or modifying the host system, ensuring that user-supplied template strings cannot execute arbitrary code.
Design Benefits
- Reusability: A single template can serve many different queries by substituting only the dynamic parts.
- Prompt engineering agility: Templates can be swapped at runtime without modifying pipeline code, enabling rapid experimentation with different prompt strategies.
- Separation of concerns: Prompt design is decoupled from application logic, allowing prompt authors and developers to work independently.
- Consistency: Templates enforce a uniform structure across all prompts, reducing the risk of malformed or inconsistent inputs to the LLM.
Example Template
template = """
Given these documents, answer the question.
Documents:
{% for doc in documents %}
{{ doc.content }}
{% endfor %}
Question: {{ query }}
Answer:
"""
In this example, the documents variable is iterated to include all retrieved document contents, while query is substituted with the user's question. The surrounding instructional text remains constant across all invocations.