Prompt Templating & Output Parsing

As AI applications grow in complexity, hard-coded prompt strings become unmanageable. This lesson covers how to use template engines for prompts, request structured outputs from LLMs, validate those outputs, and handle parsing failures gracefully.

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Why Templates?

Hard-coded prompts with string concatenation are fragile:

# Fragile — hard to maintain, easy to break
prompt = "Summarise the following " + doc_type + " about " + topic + ": " + text

Templates separate the prompt structure from the data, making prompts reusable, testable, and version-controllable.

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Template Approaches

Python f-strings (Simple)

def build_prompt(topic: str, context: str) -> str:
    return f"""You are an expert on {topic}.

Answer the following question using only the provided context.

Context:
{context}

Question: {{question}}"""

Jinja2 (Powerful)

pip install jinja2

from jinja2 import Template

template = Template("""You are a {{ role }} assistant.

{% if context %}
Use the following context to answer:
{% for doc in context %}
- {{ doc }}
{% endfor %}
{% endif %}

User question: {{ question }}

Respond in {{ format }} format.""")