Overview

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Key Challenges in Building AI Applications

Building AI applications with LLMs is easy, but ensuring consistent, high-quality performance in real-world scenarios presents significant challenges. While general-purpose LLMs can generate impressive outputs, they are not inherently fine-tuned for specific tasks, leading to:

• Task Misalignment: Generic prompts may fail to produce precise, contextually relevant responses.
• Hallucinations & Inaccuracies: Models can generate misleading or incorrect information.
• Inconsistent Compliance: Responses may not always adhere to business rules, ethical guidelines, or regulatory requirements.
• Unpredictable Performance: Variability in outputs can impact user experience and reliability.

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Importance of Evaluation

To address these challenges, systematic evaluation is essential. By assessing AI outputs based on criteria such as accuracy, relevance, coherence, factual consistency, and response efficiency, you can:

• Benchmark performance across prompts, retrieval strategies, and model versions.
• Identify weaknesses and implement targeted improvements.
• Ensure alignment with quality, compliance, and operational standards.

Comprehensive evaluation frameworks enable organisations to optimize AI behaviour, enhance user trust, and maintain robust AI-driven applications in production environments. This section is designed to help you understand, configure, and execute AI evaluations efficiently.