The Growing Importance of LLM Evaluation in AI Product Engineering (April 2025)

Introduction: Evolving Needs for LLM Evaluation

Large Language Models (LLMs) have rapidly become integral to modern AI products, but evaluating their outputs has emerged as a critical engineering challenge. Unlike traditional software, LLM behavior is probabilistic and context-sensitive, so dynamic, rigorous evaluation is needed to ensure systems meet user expectations (The Definitive Guide to LLM Evaluation - Arize AI). In 2025, AI teams are increasingly treating LLM evaluation as a first-class part of the development lifecycle. This shift is driven by real-world issues like factual hallucinations that can undermine trust or even cause serious errors if unchecked (one lawyer infamously got into real trouble after relying on a chatbot’s made-up citations (LLM Observability: The 5 Key Pillars for Monitoring Large Language Models)). As LLM deployments scale, robust evaluation pipelines are now essential to reliably measure model performance, catch failures, and continuously improve AI products (The Definitive Guide to LLM Evaluation - Arize AI). This white-paper style overview highlights emerging trends in LLM evaluation – from Retrieval-Augmented Generation testing and hallucination detection to robustness benchmarking – and describes the key capabilities, tools, and platforms shaping how teams validate LLMs in practice.

Evaluating LLMs in Retrieval-Augmented Generation (RAG)

One prominent trend is rigorous evaluation for Retrieval-Augmented Generation (RAG) systems. RAG involves feeding an LLM with retrieved documents or knowledge, enabling it to generate answers grounded in that reference data. The goal is to minimize off-base answers by anchoring the model in facts, but evaluating this reliably is non-trivial. In 2025, engineers are designing specialized RAG evaluation criteria to ensure outputs are faithful to the provided context. Key metrics include contextual recall and precision – i.e., does the LLM’s answer include the important facts from the retrieval, and does it avoid introducing unsupported details (GitHub - confident-ai/deepeval: The LLM Evaluation Framework). For example, an ideal answer should incorporate relevant information from the knowledge base (high recall) while not straying beyond it (high precision/faithfulness).

To support these goals, new tools have emerged for factual consistency checking in RAG pipelines. One approach is to use alignment models that directly judge an answer against the source documents. For instance, Vectara’s Hughes Hallucination Evaluation Model (HHEM) is an open-source classifier specifically tuned to detect whether an LLM’s summary is supported by the retrieval facts (vectara/hallucination_evaluation_model · Hugging Face). Such models take the retrieved text and the LLM’s output as input and predict if any claim in the output lacks evidence in the provided context. Notably, the latest HHEM-2.1 is lightweight (under 600MB) yet outperforms even GPT-4 at spotting factual inconsistencies (vectara/hallucination_evaluation_model · Hugging Face) – a testament to how specialized evaluators are advancing. Another example is LettuceDetect, a token-level hallucination detector designed for RAG workflows (LettuceDetect: A Hallucination Detection Framework for RAG Applications). It flags segments of an answer that are not backed by the retrieval, effectively highlighting “unsupported” sentences. LettuceDetect was trained on a large RAG consistency dataset (RAGTruth) and can handle long contexts (4k+ tokens), making it practical for real documents (LettuceDetect: A Hallucination Detection Framework for RAG Applications). By integrating these tools, teams building knowledge-base chatbots or enterprise Q&A systems can automatically verify that LLM-generated answers stay true to the source material. Evaluation in RAG thus focuses on measuring faithfulness – how well the model sticks to retrieved facts – and is becoming a standard part of RAG system development.

Hallucination Detection and Factual Consistency

Even outside of RAG, hallucination detection has become a top evaluation priority. Hallucinations refer to the LLM confidently generating information that is false or not grounded in any source. This remains an “enduring barrier” to deploying LLMs at scale (Automatic Hallucination detection with SelfCheckGPT NLI). A model that fabricates facts can erode user trust or propagate misinformation, which is unacceptable in high-stakes applications. As a result, 2025 has seen a proliferation of evaluation methods aimed at quantifying and reducing hallucinations.

Automated factuality benchmarks are now common. For example, the Hallucination Leaderboard on Hugging Face evaluates dozens of models on tasks specifically measuring factual correctness (The Hallucinations Leaderboard, an Open Effort to Measure Hallucinations in Large Language Models). It leverages an extensive benchmark suite (via EleutherAI’s LM Evaluation Harness) including open-domain QA and truthfulness tests to rank models by how much they tend to hallucinate. Such community leaderboards allow open-source and proprietary models to be compared on standardized hallucination metrics, driving improvements over time.

On the product side, engineers use a combination of LLM-based and rule-based evaluators to catch hallucinations. One powerful technique is using an LLM-as-a-judge – essentially employing a strong model (like GPT-4) to critique another model’s output against known facts or references. This “LLM-as-a-judge” approach is often the only option for subjective criteria like factual accuracy where a deterministic evaluator isn’t available (The Definitive Guide to LLM App Evaluation). For instance, given a user question and a reference answer (or a set of source documents), a GPT-4 instance can be prompted to score whether the response is correct and fully supported. Research has found that LLMs used in this manner can approximate human judgment well, and many evaluation frameworks now include GPT-based graders (sometimes called G-Eval) for tasks ranging from QA accuracy to summary factuality (GitHub - confident-ai/deepeval: The LLM Evaluation Framework). OpenAI’s own Evals framework allows developers to write custom evaluations where models are measured on arbitrary prompts and judged either by comparison to ground-truth answers or by another LLM’s assessment (OpenAI Evals - mlteam-ai.github.io).

Meanwhile, other strategies approach hallucination detection from different angles. Self-consistency methods like SelfCheckGPT work by querying the model multiple times to see if it gives inconsistent answers – large variance often signals it’s on shaky factual ground (Automatic Hallucination detection with SelfCheckGPT NLI). If an LLM is asked the same question repeatedly and it produces divergent answers, the system can flag that response as likely hallucinated (Automatic Hallucination detection with SelfCheckGPT NLI). This method interestingly treats the LLM’s own uncertainty as a red flag (and does not require an external truth source). There are also NLI-based checkers which use Natural Language Inference models to see if the generated statement can be inferred from known facts. In summary, detecting hallucinations now involves a mix of reference-based checks (comparing output to evidence) and reference-less checks (looking at output consistency or logical validity). By evaluating hallucination rates and factuality scores, teams can iterate on prompt design or fine-tuning to improve reliability before deploying LLMs to users.

Robustness and Adversarial Benchmarking

Beyond factual correctness, robustness has become a crucial dimension of LLM evaluation in engineering practice. Robustness testing asks: how does the LLM perform under stress or adversarial conditions? As LLM-powered applications move from sandbox demos to real production use, they must handle messy, unexpected inputs and resist adversarial manipulation. In 2025, we see growing efforts to benchmark and improve robustness through systematic evaluation.

One aspect is adversarial prompt testing – deliberately trying to “break” the model with tricky inputs. For example, prompt-injection attacks (where a user input tries to override the system’s instructions) are a known failure mode, and many organizations now include a battery of such attacks in their eval suite. Specialized evaluation frameworks inspired by security testing have appeared. Notably, there are LLM red-teaming platforms that provide libraries of known exploits and harmful prompts to probe models’ defenses. For instance, the Confident AI DeepTeam toolkit aligns with an “OWASP Top 10” style framework for LLM vulnerabilities (LLM Testing in 2025: Top Methods and Strategies - Confident AI). It comes with dozens of plug-and-play adversarial test cases – from jailbreak prompts that attempt to subvert content filters to input variations that target model biases – and reports on whether the model’s guardrails hold up (LLM Testing in 2025: Top Methods and Strategies - Confident AI). By running these evaluations, engineers can identify prompts that cause unwanted behavior and then adjust their models or add filtering rules accordingly.

Robustness benchmarking also includes testing model performance under input perturbations or edge cases. This might mean evaluating the model on typos or slang, out-of-distribution questions, or high-complexity multi-step queries to see if it remains stable. Some evaluation suites measure coherence across multi-turn conversations, ensuring that an LLM assistant doesn’t contradict itself or forget context as a dialogue progresses. Others evaluate how well models maintain performance when asked the same question in different phrasings or across languages. All these tests aim to surface failure modes early. Teams are increasingly treating these as regression tests: whenever a new model version or prompt update is rolled out, it is run through a robust set of stress-tests to catch regressions in reliability.

Another facet is evaluating the efficacy of tool-use and agents. Complex AI agents that plan steps or use tools (APIs, databases, etc.) add another layer to evaluate – did the agent choose the right actions to solve the task? Frameworks like Arize’s agent evaluation templates break this down (checking if the agent picked the correct tool for a query, took an efficient sequence of steps, etc.) (Agent Evaluation | Arize Docs). Metrics such as task completion rate and tool selection correctness are used to quantitatively benchmark agent-like LLM systems. In short, robustness evaluations now span from prompt attacks and reliability to agent behavior, reflecting a broader understanding of “failure modes” in LLM products. By benchmarking these, organizations can quantify their model’s resilience and steadily improve it.

Trustworthiness and Safety Metrics

Hand-in-hand with robustness, trust and safety evaluation has become a pillar of LLM assessment. Companies deploying LLMs must ensure the AI’s outputs adhere to ethical and legal standards – avoiding toxic or biased content, and respecting user instructions and policy constraints. Thus, modern LLM eval pipelines often include Responsible AI metrics that gauge an output’s safety. For example, evaluators check for toxicity, flagging if the model’s response contains offensive or harmful language (LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI). Tools like Detoxify (a BERT-based classifier) or OpenAI’s content filter model are commonly used to assign a toxicity score to each output (LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI). Similarly, bias metrics look for unfair or discriminatory content; an evaluator might scan the output for indications of racial, gender, or political bias (LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI). These metrics can be implemented via classifiers or even by prompting an LLM-as-judge with criteria like “Does this response contain any biased assumptions?” and parsing the judgment (LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI).

Another important safety aspect is hallucination of sensitive info or policy violations. An LLM might inadvertently reveal private data or give disallowed advice if not carefully controlled. To evaluate this, teams often maintain trust & safety test sets – a collection of prompts about self-harm, medical or legal advice, personal data, etc., where the correct behavior is to refuse or safe-complete. They then verify that the model’s outputs comply with the guidelines for each case. Metrics like refusal rate when appropriate and policy compliance score are tracked. For instance, Anthropic’s “Harmlessness” evaluation and OpenAI’s policy compliance evals fall in this category (ensuring the model doesn’t produce disallowed content).

Crucially, these safety evaluations are not one-time – they’re integrated continuously. Whenever a model is updated, engineers compare the new vs. old model on a suite of trust & safety prompts to ensure no regressions (e.g., the new model should not be more toxic or less compliant than the previous). If a regression is found, it may block a deployment until fixed. The emphasis on safety metrics has grown because stakeholders require evidence that an AI product won’t cause PR or harm issues. In summary, along with accuracy and robustness, responsible AI metrics like bias and toxicity are now first-class citizens in LLM evaluation, with automated scoring tools and human review processes being used to keep models’ behavior within acceptable bounds (LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI).

Integration into the Development Lifecycle

Perhaps the most impactful change by April 2025 is how LLM evaluation is woven into the software development lifecycle for AI products. In practice, this means treating evaluations almost like unit tests or integration tests, and leveraging both automation and human oversight throughout development. Several best practices have emerged:

Continuous Evaluation Pipelines: Teams now set up automated pipelines to run evaluations on each new model build or prompt change. Much like continuous integration (CI) in traditional software, these pipelines catch issues early. For example, using frameworks like DeepEval, developers create a directory of llm_tests (each test covering a scenario with expected criteria) and run deepeval test run llm_tests in CI to validate every code or model update (LLM Testing in 2025: Top Methods and Strategies - Confident AI). This ensures that a change (say a prompt tweak or parameter update) doesn’t unexpectedly break a capability or introduce a new failure mode. If any eval test fails (e.g., hallucination rate increased on a set of questions), it can block the update from release, analogous to how failing unit tests prevent a software build from deploying.
Regression and A/B Testing: When considering a new model version or prompt approach, it’s now standard to do side-by-side comparisons via evaluation suites. Teams will A/B test the incumbent model versus a candidate on a large eval set covering various dimensions (factual QA, reasoning puzzles, sensitive queries, etc.). Detailed evaluation reports are generated showing where one model is better or worse (LLM Testing in 2025: Top Methods and Strategies - Confident AI). This data-driven approach guides model upgrades – for instance, an enterprise might only switch to a new LLM model after confirming via eval metrics that it improves accuracy and maintains safety. Some platforms provide out-of-the-box support for such comparisons, making it easy to track performance across iterations.
LLM Observability and Monitoring: Once an LLM-powered system is live, evaluation continues in production via monitoring. LLM observability platforms (offered by companies like Arize, Weights & Biases, etc.) log model inputs and outputs and even compute metrics on them in real time (LLM Testing in 2025: Top Methods and Strategies - Confident AI) (LLM Observability: The 5 Key Pillars for Monitoring Large ... - Arize AI). For example, a deployed chatbot might log every response’s toxicity score or whether it triggered a hallucination detector. Dashboards then show drift in these metrics over time or flag anomalies. This closes the loop by catching new failure modes that only appear with real users. It also provides data for continuous improvement – problematic cases can be fed back into training or used to expand the evaluation test set.
Human-in-the-Loop Refinement: Automation aside, human expertise remains vital for high-quality evaluation. Many teams adopt a human-in-the-loop approach for refining LLM outputs. In practice, this means that for certain eval failures or borderline cases, human evaluators (domain experts, annotators, or even end-users via feedback) review the outputs and provide judgments or corrections. This can be done post-hoc – e.g., collecting user ratings (“thumbs up/down”) on chatbot answers and aggregating those as an evaluation signal. Some evaluation platforms now streamline human feedback collection by prompting users or reviewers and logging their responses automatically (LLM Testing in 2025: Top Methods and Strategies - Confident AI). Human review is especially important for subjective aspects like relevance or helpfulness of an answer, which might be hard to perfectly capture with automated metrics. Furthermore, the RLHF (Reinforcement Learning from Human Feedback) paradigm itself is essentially an evaluation-driven training process – using human preference scores to optimize the model. In engineering practice, after deploying an initial model, teams might periodically use human-in-the-loop evaluations to fine-tune or prompt-adjust the model for better performance. The interplay of automatic metrics and human judgment ensures evaluation remains grounded in real user preferences and values.

By incorporating these practices, organizations treat LLM quality as an ongoing, measurable deliverable. Evaluation is no longer a one-off research report; it’s a continuous process attached to each stage of product development, from prototyping to deployment and maintenance. As one guide put it, this represents a shift from traditional software testing to more “dynamic, context-sensitive evaluations” that account for LLMs’ non-determinism (The Definitive Guide to LLM Evaluation - Arize AI). The payoff is a higher level of confidence in AI behavior: issues can be caught and corrected before they affect users, and improvements can be quantified with each iteration.

Key Capabilities Driving LLM Evaluation in 2025

In summary, several key capabilities have become priorities for LLM evaluation as of 2025:

Automated Evaluation Pipelines: The ability to automatically run a battery of eval tests (accuracy, robustness, etc.) on each model update. This often integrates with CI/CD, so that every change triggers an eval job and regressions are caught early (LLM Testing in 2025: Top Methods and Strategies - Confident AI). Automation ensures evaluation is fast, repeatable, and scalable across many prompts and scenarios.
Human-in-the-Loop Refinement: Incorporating human judgment where needed – whether through curated annotation rounds or real-time user feedback. Human oversight provides nuanced ratings for complex criteria and helps refine models based on qualitative insights. Modern eval platforms even enable automating the collection of human feedback for systematic use in model improvement (LLM Testing in 2025: Top Methods and Strategies - Confident AI).
Trust and Safety Metrics: Emphasis on evaluating outputs for safety, including toxicity detection, bias assessment, and compliance with usage policies. Teams integrate responsible AI checks (for example, a pass/fail on whether any hate speech is present) as part of their eval suite (LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI). These metrics help ensure deployments align with ethical and legal standards, and they often involve both automated detectors and manual review for validation.

Each of these capabilities reinforces the others – automated pipelines handle the bulk evaluation, humans focus on the tricky parts, and safety metrics guard the boundaries of acceptable behavior. Together, they form a comprehensive evaluation regimen that is becoming standard in AI product engineering.

Tools and Platforms Gaining Adoption

To support these evaluation needs, a rich ecosystem of tools and platforms has gained adoption in both open-source and enterprise settings:

OpenAI Evals: OpenAI introduced the Evals framework, an open-source toolkit for evaluating LLMs and LLM-based systems (OpenAI Evals - mlteam-ai.github.io). It provides a registry of community-contributed evaluation tasks (covering math, coding, factual QA, etc.) and allows developers to write custom evals for their specific use cases. Many teams use OpenAI Evals to quickly bootstrap evaluation suites and benchmark different models on standard tasks.
EleutherAI LM Evaluation Harness: For research and open-source models, EleutherAI’s evaluation harness has become a go-to framework (The Hallucinations Leaderboard, an Open Effort to Measure Hallucinations in Large Language Models). It supports zero-shot and few-shot evaluation on a wide array of benchmarks (MMLU, HellaSwag, TruthfulQA, and dozens more), enabling comparison of models across many academic tasks. This harness, now actively extended by the community, underlies multiple leaderboards (including the Hugging Face Hallucination Leaderboard) to track progress on reducing issues like hallucinations.
DeepEval by Confident AI: An emerging platform popular with industry practitioners is DeepEval, an open-source framework that treats LLM tests a bit like Pytest for software (GitHub - confident-ai/deepeval: The LLM Evaluation Framework). It provides an easy way to define LLM “unit tests” (with input prompts and success criteria) and incorporates metrics from the latest research (e.g., GPT-4 based grading, hallucination detectors, and RAG-specific scores) to automatically evaluate outputs (GitHub - confident-ai/deepeval: The LLM Evaluation Framework). DeepEval can run evaluations locally (using local models or calling APIs) and integrates into CI pipelines, which has made it attractive for enterprise teams looking to add rigorous testing to their LLM apps. Confident AI also offers a cloud platform on top of this, with features for dataset management, result visualizations, and team collaboration on eval reports (LLM Testing in 2025: Top Methods and Strategies - Confident AI) (LLM Testing in 2025: Top Methods and Strategies - Confident AI).
LangChain and LangSmith: Developer libraries for building LLM applications have also added evaluation support. LangChain, for example, introduced evaluation modules and its LangSmith platform to trace LLM calls and measure performance. It allows logging of prompts and outputs during chain execution and can hook into external evaluators or user feedback to judge outcomes. This integration means that if you build a complex chain (say a multi-step question-answering workflow), you can instrument it to produce eval metrics (like correctness of the final answer, or whether each step succeeded). Such tooling lowers the barrier for developers to include evals from the start, rather than retrofitting later.
MLOps and Monitoring Platforms: Traditional ML ops tools have evolved to handle LLM-specific evaluation. For instance, Weights & Biases (W&B), known for experiment tracking, now offers LLM evaluation and monitoring capabilities. It can log evaluation metrics alongside model versions and integrates with frameworks like LangChain to provide detailed analytics during development and after deployment (Mastering LLM Evaluation: Metrics, Frameworks, and Techniques). This helps teams compare experiments and detect drifts or regressions post-deployment. Likewise, Arize AI’s Phoenix (an open-source observability tool) and similar platforms support tracing LLM decisions and attaching evaluation results (like error labels or quality scores) to production data for analysis (LLM Observability: The 5 Key Pillars for Monitoring Large ... - Arize AI). These platforms bridge the gap between offline evaluation and live monitoring, ensuring that evaluation is a continuous process.
Specialized Evaluators and Guardrails: We also see targeted tools focusing on specific evaluation aspects. Guardrails AI is an open-source library that not only helps format and validate LLM outputs but can enforce evaluation checks (e.g., ensuring JSON outputs match a schema or content is policy-compliant). There are libraries for prompt testing like PromptFoo that let developers script multiple prompts and model combinations and compare outputs quickly (often using an LLM to rank which output is best). Additionally, companies have built internal tooling for red-teaming (like prompt attack simulators) and for chain-of-thought evaluation. Many of these solutions are shared via blogs or GitHub, contributing to a rapidly maturing set of best practices.

Overall, the tooling landscape for LLM evals in 2025 is rich and growing. Open-source initiatives provide the community with common benchmarks and metrics, while commercial platforms focus on integration, scalability, and enterprise features (like data privacy, GUI dashboards, and compliance tracking). The net effect is that AI engineers now have a toolchain for LLM evaluation: from writing evals, running them at scale, to analyzing and acting on the results. This significantly accelerates the development of reliable AI products, as teams don’t need to reinvent the wheel for evaluating each new application.

Real-World Use Cases and Impact

The practical impact of these evaluation advancements is evident across industries. In enterprise settings, organizations are using LLM evals to bring AI into domains that demand high accuracy. For example, a financial services firm building a GPT-based analyst assistant will employ stringent evaluation: they use RAG with internal documents and then run nightly evals to ensure the assistant’s answers match the source filings to a very high degree of factual accuracy. Any hallucinations (e.g., an unsupported financial metric) get caught by detectors like HHEM before they reach end-users (vectara/hallucination_evaluation_model · Hugging Face). Similarly, a healthcare chatbot might be evaluated against a suite of medical Q&A pairs and checked for unsafe advice, with doctors reviewing borderline cases – a human-in-loop eval process to guarantee reliability.

Open-source communities benefit too. Developers releasing a new LLM (or a fine-tuned variant) now commonly report its eval results on standard benchmarks and even submit it to public leaderboards. This transparency helps others choose the right model for their needs (e.g., picking a model known to have low hallucination rate for a fact-heavy application). It also encourages a virtuous cycle where models compete to reduce flaws. For instance, if Model A shows a lower TruthfulQA score (meaning it outputs more falsehoods) than Model B, researchers know where to focus improvements. Over the last year, such comparisons have driven many open models to rapidly close the gap with closed models on evaluation benchmarks (vectara/hallucination_evaluation_model · Hugging Face).

Internally, product teams report that having robust evaluation in place speeds up development. Engineers can refactor a prompt or swap in a new model and get immediate feedback from the eval suite on whether the change is positive. This is especially useful when dealing with subtle prompt engineering: rather than guessing, teams treat the eval metrics (like answer relevancy or coherence) as the target to optimize (LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI). Some even integrate evaluation as an optimization step – e.g., using genetic algorithms or reinforcement learning to search for prompt variations that maximize eval scores. In short, evaluation has become part of the engineering feedback loop.

Finally, user trust and adoption of LLM applications is improved by these rigorous evaluations. When users see fewer blatant mistakes or offensive outputs, they gain confidence in the AI. Companies can point to their evaluation process as a quality guarantee. This is crucial in regulated sectors: demonstrating that “we test our model on X thousand queries and it meets these thresholds for accuracy and safety” can be part of compliance and risk assessments. As one AI cofounder noted, evaluating LLM outputs is essential to “ship robust LLM applications” (LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI) – it’s not just a research exercise, but a cornerstone of delivering AI features that work reliably in the real world.

Conclusion

By April 2025, the practice of LLM evaluation has matured from ad-hoc experiment to a comprehensive, lifecycle-focused discipline in AI product engineering. Emerging trends like RAG-specific evals, hallucination detection models, and adversarial robustness tests address the unique failure modes of large language models. At the same time, the integration of evals into CI/CD pipelines, the use of human feedback, and the tracking of trust & safety metrics ensure that LLM quality is continuously maintained and improved. The ecosystem of tools – from open benchmarks to enterprise platforms – has made it easier than ever to benchmark, debug, and refine LLM systems at scale. For professionals in the field, these developments mean that one is expected to not only build clever prompts or fine-tune models, but also to engineer a rigorous evaluation strategy around them. This combination of deep technical evaluation and practical tooling is what enables state-of-the-art LLMs to transition from the lab to dependable products. Going into a professional interview or project in 2025, one should be ready to discuss how to measure an LLM’s performance just as much as how to improve it – reflecting the growing importance of LLM evals in delivering trustworthy AI solutions.

Sources

The information above is drawn from recent industry reports, open-source project documentation, and expert blogs on LLM evaluation and best practices (The Definitive Guide to LLM Evaluation - Arize AI) (vectara/hallucination_evaluation_model · Hugging Face) (GitHub - confident-ai/deepeval: The LLM Evaluation Framework) (LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide - Confident AI), as cited throughout.