Key Metrics for Managing LLM Research Projects

How Do You Actually Measure LLM Performance- A Practical Evaluation Framework for 2025 As LLMs continue to shape enterprise AI, measuring their performance requires more than checking if the answer is “correct.” Modern evaluation spans accuracy, semantics, safety, efficiency, and human judgment. 🔍 1. Accuracy Metrics ◾ Perplexity (PPL) – How well the model predicts text (lower = better) ◾Cross-Entropy Loss – Measures prediction quality during training 📌 Useful for benchmarking probabilistic models. 🔤 2. Lexical Similarity Metrics ◾BLEU – n-gram precision ◾ROUGE (N, L, W) – n-gram recall & sequence matching ◾METEOR – Considers synonyms, stemming, word order 📌 Good for summarization and translation, but limited in capturing meaning. 🧠 3. Semantic Similarity Metrics ◾BERTScore – Uses contextual embeddings for semantic alignment ◾MoverScore – Measures semantic distance 📌 Closer to human judgment than word-based scores. 📝 4. Task-Specific Metrics ◾Exact Match (EM) – Perfect match with expected answer ◾F1 Score – Partial match overlap 📌 Ideal for QA, extraction, and structured outputs. ⚖️ 5. Bias & Fairness Metrics ◾Bias Score ◾Fairness Score 📌 Critical for high-stakes AI use cases: finance, justice, healthcare. ⚡ 6. Efficiency Metrics ◾Latency ◾Resource Utilization 📌 Required for production-grade, scalable systems. 🤝 7. Human Evaluation ◾Fluency ◾Coherence ◾Relevance ◾Toxicity & Bias 📌 Still the gold standard—automated metrics cannot fully capture nuance. 💡 Final Takeaway A robust LLM evaluation framework must combine: ◾Accuracy + Semantic Understanding + Safety + Efficiency + Human Judgment. ◾This multi-layered approach ensures trustworthy, high-performance AI systems that work reliably in production. Reference: “How to Measure LLM Performance,” Analytics Vidhya (document provided). #LLMEvaluation #AIProductManagement #GenerativeAI #MachineLearning #AIEthics #ModelEvaluation #RAG #NLP #ArtificialIntelligence #LLM #AIinBusiness #AIMetrics #DataScience #MLOps #ResponsibleAI

I have audited 50+ LLM observability dashboards over the last 18 months. The same mistake shows up every time: 10 metrics tracked at equal weight. No severity tiers. No owner chips. At 2am when something breaks, nobody knows what just paged them or who should respond. Here are the 10 metrics every LLM system must track, ranked by pager severity and assigned to the right owner 👇 🚨 𝐏𝟎 — 𝐏𝐀𝐆𝐄 𝐈𝐌𝐌𝐄𝐃𝐈𝐀𝐓𝐄𝐋𝐘 (𝐔𝐬𝐞𝐫 𝐢𝐬 𝐛𝐥𝐨𝐜𝐤𝐞𝐝) 𝟏. 𝐇𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐑𝐚𝐭𝐞 [owner: ML Eng] → Threshold: 2% gates rollback → Eval against pinned dataset, hourly ↳ The metric that ships you to the headlines. 𝟐. 𝐋𝐚𝐭𝐞𝐧𝐜𝐲 𝐏𝟗𝟗 [owner: SRE] → Threshold: SLO + 20% pages on-call → Streaming vs blocking, time-to-first-token ↳ Users abandon at 3 seconds. 𝟑. 𝐄𝐫𝐫𝐨𝐫 𝐑𝐚𝐭𝐞 [owner: SRE] → Threshold: 0.5% over 5 min → Model errors, tool failures, retries exhausted ↳ Where outages start. 𝟒. 𝐂𝐨𝐬𝐭 𝐏𝐞𝐫 𝐑𝐞𝐪𝐮𝐞𝐬𝐭 [owner: Finance + ML] → Threshold: cost ceiling per use case → Sudden spike = runaway prompt or loop ↳ The metric that pays for everything else. ⚠️ 𝐏𝟏 — 𝐃𝐀𝐈𝐋𝐘 𝐑𝐄𝐕𝐈𝐄𝐖 (𝐋𝐞𝐚𝐝𝐢𝐧𝐠 𝐢𝐧𝐝𝐢𝐜𝐚𝐭𝐨𝐫𝐬) 𝟓. 𝐓𝐡𝐫𝐨𝐮𝐠𝐡𝐩𝐮𝐭 (𝐑𝐏𝐒) [owner: SRE] → Capacity headroom, autoscale triggers ↳ Watch the trend, not the point. 𝟔. 𝐓𝐨𝐤𝐞𝐧 𝐔𝐬𝐚𝐠𝐞 [owner: ML + Finance] → Input vs output token distribution ↳ Where the cost trajectory is born. 𝟕. 𝐃𝐫𝐢𝐟𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 [owner: ML Eng] → Output distribution shift vs baseline ↳ Silent quality decay starts here. 📊 𝐏𝟐 — 𝐖𝐄𝐄𝐊𝐋𝐘 𝐑𝐄𝐕𝐈𝐄𝐖 (𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 𝐬𝐢𝐠𝐧𝐚𝐥𝐬) 𝟖. 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 [owner: ML Eng] → Top-k relevance, grounding signal ↳ Most "hallucinations" are retrieval failures. 𝟗. 𝐔𝐬𝐞𝐫 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 [owner: PM] → Thumbs up/down, structured ratings ↳ The signal that funds next quarter. 𝟏𝟎. 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 + 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 [owner: ML Eng] → Eval rubric scores, regression suite ↳ The metric you take to the board. The honest truth most LLM dashboards miss: ~ Without severity tiers, every metric becomes noise ~ Without owner chips, every alert dies in #general-eng ~ Without thresholds, every chart is a vibes check ~ Without ownership, P0 alerts have no escalation path Save this. Use it to redesign your LLMOps dashboard. Which tier is your team weakest at? 👇 Navigating job hunting or preparing for talent visa pathways?🚀  Let’s talk, schedule a free guidance session today:https://lnkd.in/g2Nvwsfb #LLMOps #AIObservability #ProductionAI #MLOps #AIEngineering

If you're building with LLMs in 2026, these are the 7 numbers that should be on your dashboard: 1. Eval pass rate — % of your regression suite passing on every deploy. If you don't have one, build it before the next feature. 2. p95 latency — not average. The slow tail is where users churn. Streaming hides this from you; measure time-to-first-token AND time-to-completion. 3. Cache hit rate — if you're not at 70%+ on prefix caching, you're burning money. Anthropic and OpenAI both charge ~10% for cached input tokens. 4. Output token ratio — cost is dominated by outputs (3-5x input price). If avg output is creeping up, your prompts are getting verbose. Tighten them. 5. Tool selection accuracy — when your agent has multiple tools, how often does it pick the right one? Below 90% means you have too many tools exposed or bad descriptions. 6. Hallucination rate on grounded queries — % of responses that contradict the retrieved context. Higher than 5% means your retrieval is failing OR your prompt isn't enforcing groundedness. 7. Cost per successful request — not per request. Failed requests + retries are your real unit economics. Track this from day one or you'll get surprised by the bill. The teams shipping serious LLM products track all 7. Most teams track 0. The gap between a demo and a product is observability. Which of these are you missing right now? Genzeon Platforms #AI #LLM #AIEngineering #MachineLearning #Observability #ProductionAI #LLMOps

Evaluating LLMs is hard. Evaluating agents is even harder. This is one of the most common challenges I see when teams move from using LLMs in isolation to deploying agents that act over time, use tools, interact with APIs, and coordinate across roles. These systems make a series of decisions, not just a single prediction. As a result, success or failure depends on more than whether the final answer is correct. Despite this, many teams still rely on basic task success metrics or manual reviews. Some build internal evaluation dashboards, but most of these efforts are narrowly scoped and miss the bigger picture. Observability tools exist, but they are not enough on their own. Google’s ADK telemetry provides traces of tool use and reasoning chains. LangSmith gives structured logging for LangChain-based workflows. Frameworks like CrewAI, AutoGen, and OpenAgents expose role-specific actions and memory updates. These are helpful for debugging, but they do not tell you how well the agent performed across dimensions like coordination, learning, or adaptability. Two recent research directions offer much-needed structure. One proposes breaking down agent evaluation into behavioral components like plan quality, adaptability, and inter-agent coordination. Another argues for longitudinal tracking, focusing on how agents evolve over time, whether they drift or stabilize, and whether they generalize or forget. If you are evaluating agents today, here are the most important criteria to measure: • 𝗧𝗮𝘀𝗸 𝘀𝘂𝗰𝗰𝗲𝘀𝘀: Did the agent complete the task, and was the outcome verifiable? • 𝗣𝗹𝗮𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Was the initial strategy reasonable and efficient? • 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Did the agent handle tool failures, retry intelligently, or escalate when needed? • 𝗠𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲: Was memory referenced meaningfully, or ignored? • 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀): Did agents delegate, share information, and avoid redundancy? • 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Did behavior remain consistent across runs or drift unpredictably? For adaptive agents or those in production, this becomes even more critical. Evaluation systems should be time-aware, tracking changes in behavior, error rates, and success patterns over time. Static accuracy alone will not explain why an agent performs well one day and fails the next. Structured evaluation is not just about dashboards. It is the foundation for improving agent design. Without clear signals, you cannot diagnose whether failure came from the LLM, the plan, the tool, or the orchestration logic. If your agents are planning, adapting, or coordinating across steps or roles, now is the time to move past simple correctness checks and build a robust, multi-dimensional evaluation framework. It is the only way to scale intelligent behavior with confidence.

Most people evaluate LLMs by just benchmarks. But in production, the real question is- how well do they perform? When you’re running inference at scale, these are the 3 performance metrics that matter most: 1️⃣ Latency How fast does the model respond after receiving a prompt? There are two kinds to care about: → First-token latency: Time to start generating a response → End-to-end latency: Time to generate the full response Latency directly impacts UX for chat, speed for agentic workflows, and runtime cost for batch jobs. Even small delays add up fast at scale. 2️⃣ Context Window How much information can the model remember- both from the prompt and prior turns? This affects long-form summarization, RAG, and agent memory. Models range from: → GPT-3.5 / LLaMA 2: 4k–8k tokens → GPT-4 / Claude 2: 32k–200k tokens → GPT-OSS-120B: 131k tokens Larger context enables richer workflows but comes with tradeoffs: slower inference and higher compute cost. Use compression techniques like attention sink or sliding windows to get more out of your context window. 3️⃣ Throughput How many tokens or requests can the model handle per second? This is key when you’re serving thousands of requests or processing large document batches. Higher throughput = faster completion and lower cost. How to optimize based on your use case: → Real-time chat or tool use → prioritize low latency → Long documents or RAG → prioritize large context window → Agentic workflows → find a balance between latency and context → Async or high-volume processing → prioritize high throughput My 2 cents 🤌 → Choose in-region, lightweight models for lower latency → Use 32k+ context models only when necessary → Mix long-context models with fast first-token latency for agents → Optimize batch size and decoding strategy to maximize throughput Don’t just pick a model based on benchmarks. Pick the right tradeoffs for your workload. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

This is how you measure your AI system as an AI Engineer 👇 For regular software you would track metrics like uptime, error rate, p95 latency. However, they say little about whether the system is fast where users feel it, affordable at scale or correct. Here are the metrics we track when building LLM systems. It is useful to group them by the question they answer: 𝟭. 𝗜𝘀 𝗶𝘁 𝗳𝗮𝘀𝘁? (𝗟𝗮𝘁𝗲𝗻𝗰𝘆) ➡️ Time to first token (TTFT): how long the user is exposed to a blank screen, the number that defines perceived latency. ➡️ Inter-token latency (ITL): how smoothly tokens stream after the first one. ➡️ End-to-end latency at p50 / p95 / p99, dominated by output length, track it per use case rather than globally. 𝟮. 𝗖𝗮𝗻 𝗶𝘁 𝘀𝗰𝗮𝗹𝗲? (𝗧𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁 𝗮𝗻𝗱 𝗰𝗼𝘀𝘁) ➡️ Tokens per second per user vs total system throughput, the two trade off against each other on the same hardware. ➡️ Input and output tokens per request to measure your unit economics. ➡️ Cache hit rate - prompt caching is often the technique that reduces cost the most. ➡️ Cost per successful task, not cost per request, a cheap request that fails is a waste. 𝟯. 𝗜𝘀 𝗶𝘁 𝗰𝗼𝗿𝗿𝗲𝗰𝘁? (𝗤𝘂𝗮𝗹𝗶𝘁𝘆) ➡️ Task success rate on a labeled eval set, re-run on every prompt or model change. ➡️ Groundedness for RAG - is the answer supported by the retrieved context. ➡️ Retrieval precision@k and recall@k - generation cannot fix what retrieval never surfaced. ➡️ LLM-as-judge scores over time, calibrated against human labels. ➡️ User feedback signals: thumbs, edits to generated output, free form feedback. 𝟰. 𝗗𝗼𝗲𝘀 𝗶𝘁 𝗵𝗼𝗹𝗱 𝘂𝗽? (𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆) ➡️ Error, timeout and rate-limit rates per provider. ➡️ Retry and fallback rate - how often you silently switch to a backup model. ➡️ Guardrail trigger and refusal rates. 𝟱. 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁 𝗯𝗲𝗵𝗮𝘃𝗲? (𝗔𝗴𝗲𝗻𝘁 𝗺𝗲𝘁𝗿𝗶𝗰𝘀) ➡️ Tool-call error rate. ➡️ Steps and tokens per completed task - drift here means cost is rising while accuracy remains the same ➡️ Context window utilization - the early warning for compaction and truncation issues. Read more about this in my newsletter: https://lnkd.in/dhiscYbm ❗️ Latency and reliability show up on day one because standard infra emits them. Quality, cost per task, and agent behavior need deliberate instrumentation, and they are where AI systems fail in production. Which metric caught a real problem for you that the standard dashboards missed? 👇

90% + of ML teams overlook the most crucial LLMOps metric: prompt monitoring. Here are 3 aspects production-ready systems track: 1/ Latency LLM answers are usually streamed token-by-token. Thus, monitoring the latency of the full answer is not enough. You have to compute: - Time to First Token (TTFT) - Time between Tokens (TBT) - Tokens per Second (TPS) - Time per Output Token (TPOT) - Total latency - Input and output tokens count 2/ Metrics Compute metrics that validate your model's performance for each input prompt and generated output tuple. Depending on your use case, you can compute % rates for: - accuracy - toxicity - hallucination - other business-specific metrics that ideally don't require labels When working with RAG systems, you can also compute metrics relative to the relevance and precision of the retrieved context. 3/ Traces Another essential thing to consider when monitoring prompts is logging their full traces. Multiple intermediate steps might be from the user query to the final general answer. Thus, logging the full trace reveals the entire process from when a user sends a query to when the final response is returned, including the system's actions, the documents retrieved, and the final prompt sent to the model. Additionally, you can log the latency, tokens, and costs at each step, providing a more fine-grained view of all the steps. . 𝗧𝗼 𝗺𝗼𝗻𝗶𝘁𝗼𝗿 these 𝟯 𝗮𝘀𝗽𝗲𝗰𝘁𝘀, you 𝗻𝗲𝗲𝗱 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝘁𝗼𝗼𝗹𝘀, such as Opik, an open-source tool maintained by Comet (already used by huge companies such as Uber and Netflix) Here is a 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝗮𝗿𝘁𝗶𝗰𝗹𝗲 showing you 𝗵𝗼𝘄 𝘁𝗼 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 using Opik: 🔗 https://lnkd.in/d4icAtxY Enjoy! #machinelearning #artificialintelligence #generativeai #mlops . 💡 Follow me for daily content on production GenAI, RecSys and MLOps.

I've spent countless hours building and evaluating AI systems. This is the 3-part evaluation roadmap I wish I had on day one. Evaluating an LLM system isn't one task. It's about measuring the performance of each component in the pipeline. You don't just test "the AI"; You test the retrieval, the generation, and the overall agentic workflow. 𝗣𝗮𝗿𝘁 𝟭: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 (𝗧𝗵𝗲 𝗥𝗔𝗚 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲) Your system is only as good as the context it retrieves. 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻: How much of the retrieved context is actually relevant vs. noise? ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗥𝗲𝗰𝗮𝗹𝗹: Did you retrieve all the necessary information to answer the query? ↳ 𝗡𝗗𝗖𝗚: How high up in the retrieved list are the most relevant documents? 𝗞𝗲𝘆 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: ↳ 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: RAGAs Framework (Repo) https://lnkd.in/gAPdCRzh ↳ 𝗣𝗮𝗽𝗲𝗿: RAGAs Paper https://lnkd.in/gUKVe4ac 𝗣𝗮𝗿𝘁 𝟮: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗟𝗟𝗠'𝘀 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲) Once you have the context, how good is the model's actual output? 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: ↳ 𝗙𝗮𝗶𝘁𝗵𝗳𝘂𝗹𝗻𝗲𝘀𝘀: Does the answer stay grounded in the provided context, or does it start to hallucinate? ↳ 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲: Is the answer directly addressing the user's original prompt? ↳ 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗙𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴: Did the model adhere to the output format you requested? 𝗞𝗲𝘆 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: ↳ 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲: LLM-as-Judge Paper https://lnkd.in/gyhaU5CC ↳ 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: OpenAI Evals & LangChain Evals https://lnkd.in/g9rjmfGS https://lnkd.in/gmJt7ZBa 𝗣𝗮𝗿𝘁 𝟯: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 (𝗧𝗵𝗲 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗦𝘆𝘀𝘁𝗲𝗺) Does the system actually accomplish the task from start to finish? 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲: Did the agent successfully achieve its final goal? This is your north star. ↳ 𝗧𝗼𝗼𝗹 𝗨𝘀𝗮𝗴𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Did it call the correct tools with the correct arguments? ↳ 𝗖𝗼𝘀𝘁/𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗽𝗲𝗿 𝗧𝗮𝘀𝗸: How many tokens and how much time did it take to complete the task? 𝗞𝗲𝘆 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: ↳ 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗔𝗗𝗞 𝗗𝗼𝗰𝘀: https://lnkd.in/g2TpCWsq ↳ 𝗗𝗲𝗲𝗽𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴(.)𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗘𝘃𝗮𝗹 𝗖𝗼𝘂𝗿𝘀𝗲: https://lnkd.in/gcY8WyjV Stop testing your AI like a monolith. Start evaluating the components like a systems engineer. That's how you build systems that you can actually trust. Save this roadmap. What's the hardest part of your current eval pipeline? ♻️ Repost this to help your network build better systems. ➕ Follow Shivani Virdi for more.

We've all shipped an LLM feature that "felt right" in dev, only to watch it break in production. Why? Because human "eyeballing" isn't a scalable evaluation strategy. The real challenge in building robust AI isn't just getting an LLM to generate an output. It’s ensuring the output is 𝐫𝐢𝐠𝐡𝐭, 𝐬𝐚𝐟𝐞, 𝐟𝐨𝐫𝐦𝐚𝐭𝐭𝐞𝐝, 𝐚𝐧𝐝 𝐮𝐬𝐞𝐟𝐮𝐥, consistently, across thousands of diverse user inputs. This is where 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 become non-negotiable. Think of them as the sophisticated unit tests and integration tests for your LLM's brain. You need to move beyond "does it work?" to "how well does it work, and why?" This is precisely what Comet's 𝐎𝐩𝐢𝐤 is designed for. It provides the framework to rigorously grade your LLM's performance, turning subjective feelings into objective data. Here's how we approach it, as shown in the cheat sheet below: 1./ Heuristic Metrics => the 'Linters' & 'Unit Tests' - These are your non-negotiable, deterministic sanity checks. - They are low-cost, fast, and catch objective failures. - Your pipeline should fail here first. ▫️Is it valid? → IsJson, RegexMatch ▫️Is it faithful? → Contains, Equals ▫️Is it close? → Levenshtein 2./ LLM-as-a-Judge => the 'Peer Review' - This is for everything that "looks right" but might be subtly wrong. - These metrics evaluate quality and nuance where statistical rules fail. - They answer the hard, subjective questions. ▫️Is it true? → Hallucination ▫️Is it relevant? → AnswerRelevance ▫️Is it helpful? → Usefulness 3./ G-Eval => the dynamic 'Judge-Builder' - G-Eval is a task-agnostic LLM-as-a-Judge. - You define custom evaluation criteria in plain English (e.g., "Is the tone professional but not robotic?"). - It then uses Chain-of-Thought reasoning internally to analyze the output and produce a human-aligned score for those criteria. - This allows you to test specific business logic without writing new code. 4./ Custom Metrics - For everything else. - This is where you write your own Python code to create a metric. - It’s for when you need to check an output against a live internal API, a proprietary database, or any other logic that only your system knows. Take a look at the cheat sheet for a quick breakdown. Which metric are you implementing first for your current LLM project? ♻️ Don't forget to repost.