Automating AI Testing: Python APIs & LLM Prompt Injection

Testing an LLM manually through a chat interface is fine for 5 prompts, but what happens when you need to test 10,000 edge cases? You need automation. In this session, we transition from manual prompt injection to building automated AI testing pipelines using Python and APIs. We explore how to connect to local and remote LLMs (like Llama 3 and Gemini), pass structured dictionaries to control system behavior, and run automated evaluations to see if a model breaks its safety guardrails. We also dive into the flaws of basic keyword testing (why searching for the word "discount" isn't enough) and how attackers use complex narrative simulations to bypass AI defenses. ⏳ Timestamps: 0:00 - The Problem with Manual LLM Testing 2:20 - Connecting to Local LLMs (Llama 3.2 via Ollama) 6:49 - Moving to Google Colab & Using the Gemini API 9:00 - Structuring API Calls: System Prompts vs. User Prompts 11:44 - Automating Tests: Running Multiple Prompts via Python For-Loops 17:46 - Flaws in Basic AI Testing: The Keyword Matching Trap 21:46 - LLM Hallucinations: When AI Makes Up Premium Services 31:41 - Advanced Prompt Injection: Exploiting AI with Storytelling & Simulations 44:26 - Why LLMs Prioritize System Prompts (And How to Override Them) 45:43 - Next Steps: Building CI/CD Pipelines for AI Evaluation Key Takeaways: Automation is King: As an AI Quality Engineer, you must shift from manual chat interfaces to orchestrating tests via Python APIs. The Dictionary Structure: Learn how to pass specific roles (system, user) to control LLM behavior prioritize instructions. Keyword Matching is Dead: Attackers don't use obvious words like "discount" or "free." They use narrative simulations and intent manipulation, requiring advanced evaluation frameworks like LLM-as-a-Judge.