Lesson 5: Building a Practical AI Workflow

Project: The Automated SummarizerIn this final project, we create a script that reads a text file and generates a concise summary. This requires integrating file handling with an AI model. Start by importing the os and openai libraries. Create a script that iterates through a directory, reads the content of each file, sends it as a prompt to the AI, and saves the result to a new summary folder. This demonstrates the practical application of [Batch Processing] and text manipulation. The output of this script will be a collection of text files representing the core concepts of your input library. Use the Python documentation for file handling reference. To further improve accuracy, incorporate a [System Prompt] that instructs the model to prioritize specific sections like the thesis or conclusion. After generating these summaries, check them against the original text for completeness. This workflow is the foundation for automating data extraction, research, and content management. Refer to W3C’s guidance on content quality to ensure your summarizations remain helpful and readable. FAQsWhat if my input file is too long?Most models have a context window limit. You must 'chunk' your text—breaking it into smaller, overlapping segments before sending it to the API. How can I verify the AI summary is accurate?You can implement a 'self-critique' step where you send the summary back to the AI with the prompt: 'Does this summary contain any information not found in the original text?' What should I do next after this course?Build a small application using a vector database to allow the AI to answer questions about your private files.