Non Functional Testing for LLM, Chatbots and AI Models

Install Visual Studio Code, the recommended code editor, and set up Java and Maven extensions, debugger, and GitHub actions to enable continuous testing and continuous integration for Java projects.

Install node.js and npm from the official site, verify with node -v and npm -v, and ensure PATH includes node and npm bin for dependency management in projects.

Install python via the microsoft store or download, version 3.11, and verify with python --version to ensure it's accessible in the terminal; the store resolves dependencies automatically for benchmark prep.

Explore the Hugging Face community page as a hub for ai professionals to access open source models, datasets, spacers, and cloud, with text classification, tokenizers, transformers, and evaluate, plus documentation.

Learn to use the Hugging Face transformers Python package, install PyTorch, and run pipelines for sentiment analysis and AI model benchmarking with pre-trained models.

Learn how to load and use any Hugging Face model, run with transformers, answer questions from context, and benchmark performance with scores, while exploring caching, diffusion models, and GPU acceleration.

Explore non-functional testing for LLMs that covers performance, security, portability, and scalability, plus ethics, adversarial testing, and assessing the model’s human-like consistency, context retention, and robustness.

Explore tuning a model end-to-end: branch, update data and code, retrain with new data and parameters, test with metrics, approve, deploy, and monitor in production.

Explains a practical lifecycle for tuning foundation models, from data preparation and model selection to training, evaluation, and fine-tuning with prompts and guardrails, including temperature and other controls.

Model training time drives non-functional performance, influenced by data size and hyperparameter tuning. Using ChatGPT as an example, it shows how epochs and learning rate affect training duration and cost.

Measure inference time to gauge how quickly a generative ai model returns predictions. For chatbots and apps requiring real-time responses, consider performance testing, response time, and infrastructure effects on inference.

Explore model drift and decay, where performance worsens over time due to bad data from interactions or new data emerging, and learn to monitor, benchmark, and retrain with fresh data.

Size ai infrastructure with performance testing and model distillation to run powerful yet small models, including examples of running on the iPhone 15 with cpu, gpu, and response-time considerations.

Examine memory and context in chatbots, showing how prompts carry conversation history, how input and output tokens set model limits, and how to manage memory with boundary value analysis.

Discover that a token is a unit of text in LLMs, which can be characters, words, or spaces, and that prompts are parsed into tokens using a tokenizer tool.

Obtain and securely manage your OpenAI API key tied to your account, create a new secret key named demo, and use it for all demos and ChatGPT requests.

Learn how to build an automated test suite for chatbots and api collections, structure prompts to yield deterministic results, and validate responses with restricted outputs, scripts, and a ci/cd workflow.

Export your Postman collection, install Newman, and run tests from the command line to automate non functional testing and handle API keys via environment variables in CI/CD.

Explore privacy leakage in large language models by examining memorized training data and context leakage, with techniques like adversarial testing, data reduction, prompt injection, model inversion, and membership inference.

Explore bias in bios evaluation benchmarks by testing how AI systems predict professions from contextual bios, comparing male and female predictions to measure delta and identify gender bias.

Select a text-focused model such as Roberta, using Hugging Face transformers to download the model and tokenizer, and prepare the data by tokenizing it for the model.

Understand bias in bias data and its data structure to guide fine tuning and benchmarking of LLMs, analyzing train, dev, and test splits on hugging face with profession labels.

Explore training a fine-tuned BERT model on 500 shuffled data with validation, exclude gender to reduce bias, and benchmark accuracy using evaluate scripts.