How to Build an AI Text Summariser with Python and Claude | Part 1
Sathish
16 views
Build an AI text summariser with Python and Claude, starting with user stories and acceptance tests, then working through the model integration.
This is a full, roughly 70-minute build-along using Codex. I’m learning AI engineering as I build: asking questions, comparing options, and working through the mistakes and decisions along the way.
We start with a simple goal: paste a passage into a webpage and receive a shorter summary that preserves its main points.
Along the way, we cover:
Turning that goal into a user story and concrete acceptance tests.
Keeping the model request and API key in the backend.
Using explicit instructions and checking measurable rules in code.
Evaluating accuracy, main-point coverage, length, and writing style separately.
Why a high overall score can hide a factual error.
Switching an existing OpenAI integration to Claude.
Working through the system instruction, max_tokens, and generated-text extraction in Python.
The session starts from an existing repository and application scaffolding. We work toward the first real summary in the webpage; the broader quality-scoring design is still an experiment, with further implementation and testing to do.
If you’re also learning to build AI applications, this is a look at the reasoning and troubleshooting behind an early version of a project.
Chapters
00:00 Learning-in-public build introduction
03:17 User stories, tests, and implementation
08:42 Defining acceptance tests for the summariser
15:02 Backend responsibilities and AI system design
19:46 Writing explicit summary instructions
21:32 Code checks versus AI evaluation
27:06 Handling summaries that fail checks
28:47 Quality scores and acceptance thresholds
35:40 Requiring a score for each quality dimension
40:20 Moving from design to the first test case
44:35 Returning only the finished summary
47:52 Switching the integration to Claude
51:16 Running the local application
58:29 Supplying the instruction to Claude
1:02:01 Choosing the output token limit
1:06:22 Extracting the generated summary text
1:09:14 Checking the local response preview
How to Build an AI Text Summariser with Python and Claude | Part 1
Sathish
16 views
Build an AI text summariser with Python and Claude, starting with user stories and acceptance tests, then working through the model integration.
This is a full, roughly 70-minute build-along using Codex. I’m learning AI engineering as I build: asking questions, comparing options, and working through the mistakes and decisions along the way.
We start with a simple goal: paste a passage into a webpage and receive a shorter summary that preserves its main points.
Along the way, we cover:
Turning that goal into a user story and concrete acceptance tests.
Keeping the model request and API key in the backend.
Using explicit instructions and checking measurable rules in code.
Evaluating accuracy, main-point coverage, length, and writing style separately.
Why a high overall score can hide a factual error.
Switching an existing OpenAI integration to Claude.
Working through the system instruction, max_tokens, and generated-text extraction in Python.
The session starts from an existing repository and application scaffolding. We work toward the first real summary in the webpage; the broader quality-scoring design is still an experiment, with further implementation and testing to do.
If you’re also learning to build AI applications, this is a look at the reasoning and troubleshooting behind an early version of a project.
Chapters
00:00 Learning-in-public build introduction
03:17 User stories, tests, and implementation
08:42 Defining acceptance tests for the summariser
15:02 Backend responsibilities and AI system design
19:46 Writing explicit summary instructions
21:32 Code checks versus AI evaluation
27:06 Handling summaries that fail checks
28:47 Quality scores and acceptance thresholds
35:40 Requiring a score for each quality dimension
40:20 Moving from design to the first test case
44:35 Returning only the finished summary
47:52 Switching the integration to Claude
51:16 Running the local application
58:29 Supplying the instruction to Claude
1:02:01 Choosing the output token limit
1:06:22 Extracting the generated summary text
1:09:14 Checking the local response preview