One of the current trends is the implementation of AI into quality assurance (QA) processes. AI promises a leap in ticket coverage, uniformity and cost savings.
In many organizations however, the thought of AI is rather frightening as AI can get rather complex.
This guide aims to demystify the process, highlighting key steps to execute an experimental pilot. We hope to shine light on the unknown - which is often scarier in imagination than reality.
Advantages of AI-Led QA
Superhuman Efficiency and Consistency: AI systems can evaluate large numbers of tickets quickly AND be consistent without the need for a calibration process.
Greater Precision and Less Errors: AI-led QA improves accuracy by eliminating human bias and fatigue.
Higher Volumes at Low Marginal Cost: AI-led QA’s costs do not grow proportionately with volume, unlike human-led QA.
Navigating Challenges
Implementing AI-led QA is more than the mere application of chatGPT. It requires a thoughtful approach to AI infrastructure setup and deep knowledge of advanced AI techniques.
Steps Towards AI-QA Readiness
Data Preparation:
Collecting and organize existing historical QA data that will be used to both train AND evaluate your AI models.
Translating QA Scorecards into AI Pipelines:
Break down your QA goals into quantifiable, step-by-step logical processes.
Refine or redefine existing metrics to ensure they’re measurable.
Match the right AI tool or tools to each metric for optimal results.
Training and Building LLM Chains:
Fine-tune your AI models or engineer prompt chains using your prepared data.
Pre-Deployment Evaluation:
Examine the AI system on a “golden dataset” that contains what you would deem as “correct” answers.
Small-Scale Live Testing:
Compare AI-led QA results against human-led QA.
Fix bugs and adapt for edge cases
Live Deployment with Continuous Evaluation:
Implement monitoring of performance in real-time.
Establish a feedback loop for ongoing system improvement.
The Need for “Translating” Into AI
There are three reasons why it is practically impossible to just throw scorecard questions into chatGPT and obtain a good outcome:
LLMs are very good at single, discrete tasks. As such, breaking down goals into step-by-step thinking processes, each of which are quantifiable, can improve performance by magnitudes.
Poorly designed scorecard metrics are problematic. They lack clarity in what’s being measured and how to distinguish good from bad performance. AI amplifies flaws in logic and ambiguity, so “polishing up” QA scorecards is a critical initial step.
LLMs are powerful tools, but optimal results require matching various advanced AI techniques to specific types of tasks. Accurate and consistent results can then be guaranteed to a large degree.
Critical Considerations
Golden Dataset: A set of “correct” answers is essential to both train and establish benchmarks for your AI system.
Quantitative Metrics: it is crucial to define what constitutes good and bad performance, specifically, in order to really leverage the power of AI.
Continuous Monitoring: AI systems themselves require checks to verify system performance and identify any outlier input data or unexpected behaviours.
Continuous Improvement: View AI-led QA as an ongoing process, not a one-time deployment. Regular refinement based on your specific data will unlock a significant competitive advantage that only gets stronger over time.
If you follow this roadmap and keep the above considerations in mind, deploying AI in the QA process is very straightforward. We help companies implement AI-led QA and compliance checks processes, and take ticket coverage from 5% to 100%. If you’re keen to learn more, let’s chat.
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