Day 1: One-Step Summary Chain

Day 1: One-Step Summary Chain

Yesterday I found myself wrestling with a 1,200-word blog post, trying to boil it down to a single, Twitter-ready summary. I pasted, prompted, tweaked, pasted again…five times. It felt like hammering nails with my forehead.
That’s when I realized: if I’m going to be chaining LLM calls every day, I need a pattern. Something I can reuse, parameterize, and trust not to explode in my face at 2 AM.

2. Mental Model: Assembly Line in Code

LangChain = pipelines of specialists.
Picture an assembly line: raw text in → “Summarizer” station does its job → polished summary out the other end. Today we’ll build that single “Summarizer” station.

3. The Code: Under 10 Lines to Your First Chain

## Day 1: One-Step Summary Chain

A Jupyter notebook for quickly experimenting with your first LangChain pipeline.
### Install & Setup
# Install dependencies (run this cell once)


! pip install -U langchain langchain-core langchain-openai
# Imports & setup

from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI # Or ChatOpenAI for chat models

import os
os.environ['OPENAI_API_KEY'] = '******'

# Initialize the LLM wrapper
llm = OpenAI(temperature=0.5) # balance creativity vs. focus

# Define a reusable prompt template
template = PromptTemplate(
    input_variables=["text"],
    template="Summarize the following article in one sentence:\n\n{text}"
)

# Create the chain
chain = template | llm

# Define your article
article = """
LangChain is a framework for building LLM-powered pipelines...
[your full text here]
"""

# Run the chain and print the result
summary = chain.invoke({"text": article})
print("🔹 One-sentence summary:", summary)

4. Line-by-Line Breakdown

1. OpenAI(temperature=0.5)
   We wrap the model with a mild temperature to keep outputs coherent but not robotic.
2. PromptTemplate
   Instead of hard-coding, we define a blueprint with a {text} placeholder—so we can swap in any article at runtime.
3. LLMChain
   Think of this as “LLM + prompt glued together.” You call .run(), and LangChain handles rendering the prompt, shipping it off to the API, and returning the output.
4. chain.run(article)
   Feeds your article into the pipeline and yields the one-sentence summary.

5. Trying It Out

Example Input:
“LangChain is a framework for orchestrating beautiful pipelines with LLMs, letting you stitch together prompt templates, chaining, agents, memory, and tools…”

Example Output:
“LangChain is a Python library that helps you build reusable, maintainable pipelines of prompt templates and large language models.”

(Your mileage may vary—tweaking temperature, prompt wording, or LLM choice can nudge the tone.)

6. Takeaway

• You just built a reusable “summarizer” module in under 10 lines.
• Next steps: Chain this summarizer with sentiment analysis or keyword extraction so you can process an entire blog post in one go.
• Broader lesson: Whenever you find yourself copy-pasting or repeating prompts, reach for a PromptTemplate and LLMChain. That simple pattern will save you hours (and a lot of typos).

Tomorrow: we’ll take this one-step chain and morph it into a classification pipeline—because why stop at summarizing when you can also tag sentiment? See you on Day 2!