Basic Python scripting for data extraction and LLM prompt testing
The application of Python to programmatically collect, parse, and structure data from diverse sources (APIs, databases, files) and to systematically design, execute, and evaluate prompts against Large Language Models.
It enables data-driven product development and research by automating data pipelines and rigorously testing LLM capabilities, directly reducing operational overhead and accelerating the iteration cycle for AI-powered features. This skill directly translates to faster prototyping, improved model performance, and scalable AI integration within business processes.
1Careers
1Categories
8.7 Avg Demand
25%
Avg AI Risk
How to Learn Basic Python scripting for data extraction and LLM prompt testing
1. Master Python fundamentals: variables, data types (lists, dicts), control flow (if/else, for loops), and functions. 2. Learn core data extraction libraries: `requests` for HTTP APIs, `BeautifulSoup`/`lxml` for HTML parsing, and `pandas` for reading CSV/Excel files. 3. Understand the basics of LLM interaction via API, focusing on the structure of an API request (endpoint, headers, JSON payload) and the OpenAI Chat Completions API format.
Move to practice by building end-to-end scripts that chain extraction, transformation, and LLM interaction. Focus on handling pagination for APIs, managing authentication (API keys, tokens), and implementing robust error handling and logging. A common mistake is neglecting data validation post-extraction, leading to garbage-in-garbage-out for LLM prompts. Practice designing prompt templates with clear instructions, role definitions, and few-shot examples.
Architect scalable and maintainable data/LLM pipelines. This involves designing modular code with abstracted data source connectors and prompt registries, implementing async operations for concurrent API calls (`aiohttp`, `asyncio`), and building simple evaluation frameworks to quantitatively measure LLM output quality (e.g., using regex, string matching, or smaller classifier models). Mentoring involves establishing best practices for secure secret management and reproducible environments (`docker`, `venv`).
Practice Projects
Beginner
Project
Scrape a Static Website and Summarize Content
Scenario
Extract all article headlines and summaries from a simple news blog's homepage, then use an LLM to generate a one-paragraph digest of the top 5 stories.
How to Execute
1. Use `requests` to GET the HTML page. 2. Parse the response with `BeautifulSoup`, using `.find_all()` with appropriate CSS selectors. 3. Clean the text and construct a list of headlines. 4. Craft a prompt with the list, send it to the OpenAI API using `openai` library, and print the summary.
Intermediate
Project
Build a Multi-Source Product Review Aggregator & Sentiment Analyzer
Scenario
Extract product reviews from two different e-commerce sites (requiring different parsing logic) via their public APIs or HTML. Aggregate them, then use an LLM to classify each review's sentiment (positive/negative/neutral) and extract key themes.
How to Execute
1. Design a class or function for each data source that returns a standardized list of review dictionaries. 2. Handle pagination and rate limiting in each extractor. 3. Merge the lists and use a loop to send each review text to an LLM with a structured prompt for JSON output containing 'sentiment' and 'themes'. 4. Use `pandas` to load the LLM's JSON responses, analyze sentiment distribution, and report on common themes.
Advanced
Project
Automated RAG Prompt Engineering and Evaluation Pipeline
Scenario
Develop a system that ingests a corpus of internal documents (PDFs, web pages), chunks them, stores embeddings in a vector DB, and then runs a suite of test queries through a Retrieval-Augmented Generation (RAG) pipeline. The system must evaluate answer quality against ground-truth answers.
How to Execute
1. Implement document loaders and text splitters (`langchain`). Use an embedding model (e.g., OpenAI, Cohere) to create and store vectors in a database (e.g., FAISS, Pinecone). 2. Build a prompt template that includes a `{context}` variable. 3. Create a test harness with a set of (query, ground_truth) pairs. 4. For each test, run retrieval, format the prompt, call the LLM, and use a scoring function (e.g., ROUGE, exact match, or a separate LLM-as-judge call) to compare the generated answer to the ground truth. Log results for analysis.
Tools & Frameworks
Core Python Libraries for Extraction
requestsBeautifulSoup / lxmlpandasjson
`requests` for HTTP calls. `BeautifulSoup`/`lxml` for parsing HTML/XML. `pandas` for tabular data manipulation and I/O (CSV, Excel, SQL). `json` for handling API payloads.
LLM Interaction & Prompt Engineering
openai (Python SDK)langchaintiktoken
`openai` is the official SDK for calling OpenAI and compatible APIs. `langchain` provides higher-level abstractions for chains, agents, and document loaders. `tiktoken` is for counting tokens to manage context window limits and cost.
Environment & Deployment
python-dotenvvirtualenv / venvDocker
`python-dotenv` for loading API keys from .env files securely. `virtualenv`/`venv` for dependency isolation. `Docker` for creating reproducible runtime environments for scripts and microservices.
Interview Questions
Answer Strategy
The interviewer is assessing system design, robustness, and practical problem-solving. Structure your answer: 1) **Data Ingestion & Preparation**: Use `pandas` to read the CSV, clean URLs. 2) **Extraction Script Design**: For each URL, use `requests` with a timeout and proper User-Agent. Parse HTML with `BeautifulSoup`. 3) **Challenge Identification**: Explicitly mention inconsistent page structures, JS-rendered content (requiring `selenium` or `playwright`), CAPTCHAs, and anti-bot measures. 4) **Error Handling**: Implement try-except blocks for network errors (requests.exceptions) and parsing errors. Log failures (e.g., HTTP 404, 500, no email found) to a separate file for manual review. Consider a fallback regex pattern for emails if a structured selector fails. 5) **Output**: Store successful extractions and failed URLs separately.
Answer Strategy
Testing methodical prompt engineering and evaluation. Core competency: structured experimentation. Sample Response: 'I would approach this iteratively. First, I'd create a benchmark set of 10-15 representative invoice texts with manually labeled ground-truth JSON outputs. I'd start with a simple, direct instruction prompt: "Extract the invoice data as JSON." I would analyze failure cases-perhaps the model hallucinates fields or misinterprets dates. Then, I would iterate by adding explicit chain-of-thought: "First, identify the vendor. Second, list all line items..." and by providing 2-3 few-shot examples of correct input-to-JSON transformation. For each prompt version, I would run the full benchmark, programmatically compare the LLM's JSON output to the ground truth using a metric like exact match for key fields or a structural similarity score. I would select the prompt version that maximizes accuracy on the benchmark, not just a single example.'
Careers That Require Basic Python scripting for data extraction and LLM prompt testing