This guide to AI evals was written by Aakash Gupta, based on an episode with Ankit Shukla, published February 19, 2026.
Why AI Evals Matter for PMs
AI evaluations are a critical new skill for all Product Managers because AI functions differently from traditional deterministic code.
AI is Probabilistic
Unlike traditional code that is deterministic (e.g., 2+2 always equals 4), AI outputs are probabilistic and can vary.
Traditional Testing Insufficient
The probabilistic nature of AI means traditional feature testing methods are insufficient for AI features.
Three-Part Eval System
A comprehensive AI evaluation system requires three types of evaluations: offline, online, and human.
Offline Evals
Offline evals are used for testing AI features before they are launched.
Online Evals
Online evals are used for monitoring AI feature performance after launch in production.
Human Evals
Human evals involve spot-checking quality to determine if users actually like the AI feature.
Need All Three Evals
Teams commonly skip online and human evals, but all three types are necessary to prevent feature failure.
PMs Should Own Evals
AI product managers should own evaluations due to their unique position understanding business, customers, and technology outcomes.
PMs Understand Outcomes
Product Managers understand what success looks like, customer needs, and business value metrics.
Industry Leaders Agree
Product leaders like Todd Olson (CEO of Pendo) and Rachel Wolan (CPO of Webflow) agree that AI evals are the most important new skill for PMs.
Cost of Not Doing Evals
Skipping AI evaluations can lead to features failing in production, increased support tickets, and wasted development effort.
Failed Feature Scenario
A prototype works great in demo but fails after launch, causing user complaints, hallucinations, and a feature rollback.
Work Lost
Six months of work can be lost if a feature is rolled back due to lack of proper evaluations.
Fundamental Nature of LLMs
Understanding how Large Language Models (LLMs) fundamentally work is crucial before building effective evaluations.
LLMs Are Probabilistic
LLMs are statistical models that predict the next token based on probability distributions, not by 'knowing' facts.
Capital of France Query
When asked 'What’s the capital of France?', an LLM predicts 'The capital of France is Paris' based on probability, not a lookup.
Different Outputs Explanation
The same prompt can yield different results because the model samples from a probability distribution, leading to varied outputs.
The Temperature Problem
Temperature controls the randomness of LLM outputs, ranging from deterministic (Temperature = 0) to more random (Temperature = 1).
Typical Production Temperature
Most products use a temperature setting between 0.3 and 0.7 for LLMs.
Temperature Impact on Evals
Evaluations must be conducted at the same temperature setting used in production, or they become meaningless.
The Context Window Problem
LLMs have limited context windows, and response quality degrades as the window fills, especially for information in the middle.
GPT-4 Context Window
GPT-4 has a context window of 128K tokens.
Claude Context Window
Claude has a context window of 200K tokens.
Lost in the Middle Problem
Critical information placed in the middle of a long prompt might be missed by the LLM, even within the context window.
Context Length Impact on Evals
Evaluations must test different context lengths, as prompt performance can vary significantly.
The Prompt Sensitivity Problem
Small changes in prompts can lead to large and unpredictable changes in LLM outputs, making AI products fragile.
Prompt Variation Example
Changing 'Please summarize this document' to 'Summarize this document' or adding 'Be concise' yields different results.
Prompt Variation Impact on Evals
Evaluations must test multiple prompt variations that users might actually type, not just one canonical prompt.
The Hallucination Problem
LLMs can hallucinate, making up facts, citing non-existent sources, and inventing details because they predict plausible, not accurate, text.
Verification Required
It is essential to verify that LLM output is factually correct, not just that it 'looks good'.
Build Evaluation Rubric
Building an evaluation rubric is essential to define 'good' quality and measure the performance of AI features.
Start with User Scenarios
Begin rubric development by identifying the top 10 scenarios users will encounter with the AI feature.
Chatbot Scenarios
For a customer support chatbot, scenarios include questions about return policy, shipping times, product specs, account issues, and unclear queries.
Code Generation Tool Scenarios
For a code generation tool, scenarios include requests for simple functions, complex algorithms, refactoring, bug fixes, and tests.
User Scenarios as Test Cases
The identified user scenarios become the test cases for evaluating the AI feature.
Define Success Criteria
For each scenario, define specific, measurable, and unambiguous criteria for what constitutes a successful AI response.
Good vs Bad Criteria
Bad criteria: 'The response is helpful'; Good criteria: 'The response contains the correct return window (30 days) and includes the return portal link'.
Code Success Criteria
Good code success criteria: 'The code passes all test cases, follows project style guide, and includes error handling'.
Build Rubric Categories
A good rubric typically has 4-6 categories, each with a 1-5 scoring scale.
Rubric Categories
Key categories include Correctness, Completeness, Clarity, Tone, Safety, and Efficiency.
Scoring Scale Definition
The 1-5 scale ranges from 'Completely fails' (1) to 'Fully succeeds' (5), with clear definitions for each score.
Create Reference Examples
For each category and score level, create reference examples to establish ground truth for evaluators.
Correctness Example Scores
Examples for 'Correctness' in a support chatbot demonstrate scores of 5 (full success), 3 (partial success), and 1 (factual error).
Reference Examples Purpose
These examples show human or LLM evaluators what specific quality levels look like.
Test Your Rubric
Have 2-3 people independently grade the same 10 outputs using the rubric and calculate inter-rater reliability to refine it.
Reliable Evals Need Good Rubric
A bad rubric leads to bad evals, while a good rubric ensures reliable evaluations.
Evaluation Metrics Framework
Different AI use cases require specific metrics for effective evaluation, as there is no 'one size fits all' approach.
Retrieval Metrics
Metrics for AI systems that retrieve information, such as RAG systems, focus on relevance and surfacing the right documents.
Key Retrieval Metrics
Key metrics include Precision, Recall, F1 Score (harmonic mean), Mean Reciprocal Rank (MRR), and Normalized Discounted Cumulative Gain (NDCG).
Most Important Retrieval Metrics
For most AI Product Manager use cases, F1 and NDCG are the most important retrieval metrics.
Generation Metrics
Metrics for AI systems that generate text, focusing on similarity and overlap with reference texts.
Text Generation Metrics
Metrics include BLEU (translation), ROUGE (summarization), METEOR (synonyms/stemming), and BERTScore (semantic similarity).
Most Robust Generation Metric
BERTScore is recommended as the most robust generation metric for most AI products.
Task-Specific Metrics
Some AI tasks require custom metrics tailored to the specific requirements and desired outcomes of that task.
Code Generation Metrics
Metrics for code generation include compilation success, test passage, style guide adherence, and cyclomatic complexity.
Customer Support Metrics
Metrics for customer support include correct information, required links, brand tone matching, and issue resolution.
Summarization Metrics
Metrics for summarization include capturing key points, omitting irrelevant details, length, and coherence.
LLM-as-Judge Metrics
Using an LLM to grade outputs based on a rubric can correlate better with human judgment than traditional metrics.
LLM Judge Method
Ask an LLM (e.g., GPT-4 or Claude) to score a response on a 1-5 scale for helpfulness or other criteria.
Good Rubric is Key for LLM Judge
The effectiveness of an LLM judge relies on providing it with a well-defined rubric and reference examples.
Choosing the Right Metrics
The choice of metrics depends on the AI's primary function, often requiring a combination of multiple metrics.
Metrics Decision Tree
Use Precision/Recall/F1 for document retrieval, BERTScore for generating text similar to references, task-specific metrics for specific tasks, and LLM-as-judge for holistic quality.
Build LLM Judges Step-by-Step
Implementing LLM judges involves a step-by-step process from defining evaluation prompts to continuous evaluation.
Step 1: Define Evaluation Prompt
The evaluation prompt for an LLM judge needs four components: the rubric, reference examples, input query, and output to evaluate.
Evaluation Prompt Template
A template includes criteria like Correctness, Completeness, Clarity, and Tone with 1-5 scoring, reference examples, user query, and assistant response.
Step 2: Test on Known Examples
Manually test the LLM judge with 10 outputs that have known correct scores, comparing judge scores to ground truth and refining the prompt as needed.
Common Prompt Fixes
Common fixes include adding more reference examples, making criteria more specific, adding chain-of-thought reasoning, or using a better model (e.g., GPT-4 vs GPT-3.5).
Step 3: Implement with Claude Code
Automate the eval pipeline using tools like Claude Code by providing the evaluation prompt and test dataset to generate the necessary script.
Claude Code Prompt
A prompt for Claude Code can request an eval pipeline taking a CSV, running through Claude as a judge, parsing scores, and outputting a summary CSV with averages and flagged low scores.
PM Role in Implementation
The PM's role is defining the rubric, curating test cases, and interpreting results, while implementation is largely automated.
Step 4: Read Results and Find Gaps
Generate a summary dashboard from eval results, focusing on mean scores, distributions, and worst/best performing examples to identify patterns.
Claude Code Dashboard Request
Tell Claude Code to generate a dashboard showing mean score per criterion, score distribution, and top 10 worst/best performers.
Product Sense for Gaps
Product sense is crucial to interpret numbers, determine why failures occurred, and decide on corrective actions.
Step 5: Set Quality Thresholds
Define minimum acceptable scores for each criterion and add them as pass/fail gates in the evaluation pipeline.
Example Thresholds
Example thresholds include Correctness and Completeness ≥4.0 average, and Clarity and Tone ≥3.5 average.
Claude Code Threshold Integration
Instruct Claude Code to add pass/fail checks to the eval pipeline, flagging runs if any criterion average drops below defined thresholds.
Step 6: Run Evals Continuously
Implement continuous evaluation before every release, daily in production, after prompt changes, and after model updates to catch regressions.
Claude Code Continuous Eval Setup
Tell Claude Code to run the eval pipeline nightly against a 1% random sample of production traffic and send Slack notifications for threshold drops.
Common LLM Judge Pitfalls
Several common pitfalls can undermine the effectiveness of LLM judges if not addressed.
Pitfall 1: Same Model for Judge/Product
Using the same model as both the judge and the product can lead to biased evaluations.
Solution: Stronger Judge Model
Use a stronger model as the judge (e.g., GPT-4 to judge GPT-3.5 outputs).
Pitfall 2: Not Calibrating Judge
Failing to calibrate the LLM judge regularly can result in unreliable scores.
Solution: Calibrate Regularly
Regularly compare judge scores to human scores and adjust prompts to ensure accuracy.
Pitfall 3: Too Many Dimensions
Judging too many dimensions at once can overwhelm the LLM judge and reduce accuracy.
Solution: Break Down Rubrics
Break complex rubrics into multiple judge calls to evaluate fewer dimensions per call.
Pitfall 4: Non-Zero Temperature
Using a temperature greater than 0 for LLM judges introduces randomness, making scores inconsistent.
Solution: Use Temperature=0
Always use temperature=0 for LLM evaluations to ensure deterministic and consistent scores.
Production Monitoring That Works
Effective AI evaluation extends beyond launch with robust production monitoring to ensure ongoing performance and value.
Three Layers of Monitoring
Production monitoring involves three layers: System Metrics, Quality Metrics, and Business Metrics.
Layer 1: System Metrics
These are basic health metrics indicating infrastructure issues.
Examples of System Metrics
System metrics include Latency (p50, p95, p99), Error rate, Token usage, API costs, and Timeout rate.
Layer 2: Quality Metrics
These measure the performance and quality of the AI's output.
Examples of Quality Metrics
Quality metrics include average LLM judge scores, human feedback scores (thumbs up/down), task success rate, and hallucination rate.
Layer 3: Business Metrics
These measure if the AI is delivering value and achieving business objectives.
Examples of Business Metrics
Business metrics include feature adoption rate, user retention, customer satisfaction (CSAT/NPS), support ticket deflection, and revenue impact.
All Layers Are Critical
All three layers are necessary; ignoring any layer leaves gaps in understanding AI performance and value.
Setting Up Automatic Alerts
Establish alerts for critical performance deviations to enable immediate investigation.
Alert Criteria Examples
Alerts should be set for quality scores dropping below threshold, error rates spiking above 1%, p95 latency exceeding 3 seconds, or hallucination rate exceeding 5%.
The Human Review Queue
Sample 1% of production traffic daily for human review of 10-20 real user interactions using the rubric.
Human Review Catches Misses
Human review catches issues LLM judges miss and keeps the team connected to real user experiences.
Recalibrate Judge if Divergent
If human scores diverge from LLM judge scores, recalibrate the LLM judge.
The Feedback Loop
Production monitoring findings should feed back into the evaluation dataset to continuously improve evals.
Feedback Loop Steps
Add bad production outputs to the test dataset, label them with correct scores, and rerun evals to ensure the system catches them.
Virtuous Cycle of Evals
This feedback loop creates a virtuous cycle, continuously improving the evaluation system over time.
When to Rollback
Define clear rollback criteria beforehand to quickly reverse a feature if it fails in production.
Rollback Criteria Examples
Criteria include quality score drop >10% from baseline, error rate exceeding 5%, more than 3 critical bugs in 24 hours, or negative business impact.
Final Words: Key Actions
AI evals combine product sense, technical understanding, and statistical thinking, forming a new essential PM skill.
Build Eval Rubric
Build a comprehensive evaluation rubric specifically for your product.
Implement Right Metrics
Implement appropriate metrics, including retrieval, generation, task-specific, and LLM-as-judge metrics.
Create LLM Judge
Create an LLM judge to automate the evaluation process.
Set Quality Thresholds
Establish clear quality thresholds before launching any AI feature.
Monitor Quality Continuously
Monitor AI quality continuously once it is in production.
Feedback Production Learnings
Integrate learnings from production back into your evaluation dataset for ongoing improvement.
Do Not Ship Without Evals
It is crucial never to ship an AI product without a robust evaluation system in place.
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APEX
AI Evals Explained Simply
Ankit Shukla provides an intuitive walkthrough of AI evaluations, highlighting their importance for Product Managers.
This guide to AI evals was written by Aakash Gupta, based on an episode with Ankit Shukla, published February 19, 2026.
▸ 5 Expand
CONC
Why AI Evals Matter for PMs
AI evaluations are a critical new skill for all Product Managers because AI functions differently from traditional deterministic code.
·
JUST
AI is Probabilistic
Unlike traditional code that is deterministic (e.g., 2+2 always equals 4), AI outputs are probabilistic and can vary.
·
INSG
Traditional Testing Insufficient
The probabilistic nature of AI means traditional feature testing methods are insufficient for AI features.
▸ 4 Expand
CONC
Three-Part Eval System
A comprehensive AI evaluation system requires three types of evaluations: offline, online, and human.
·
SUBC
Offline Evals
Offline evals are used for testing AI features before they are launched.
·
SUBC
Online Evals
Online evals are used for monitoring AI feature performance after launch in production.
·
SUBC
Human Evals
Human evals involve spot-checking quality to determine if users actually like the AI feature.
·
INSG
Need All Three Evals
Teams commonly skip online and human evals, but all three types are necessary to prevent feature failure.
▸ 2 Expand
CONC
PMs Should Own Evals
AI product managers should own evaluations due to their unique position understanding business, customers, and technology outcomes.
·
JUST
PMs Understand Outcomes
Product Managers understand what success looks like, customer needs, and business value metrics.
·
DETL
Industry Leaders Agree
Product leaders like Todd Olson (CEO of Pendo) and Rachel Wolan (CPO of Webflow) agree that AI evals are the most important new skill for PMs.
▸ 2 Expand
CONC
Cost of Not Doing Evals
Skipping AI evaluations can lead to features failing in production, increased support tickets, and wasted development effort.
·
EXMP
Failed Feature Scenario
A prototype works great in demo but fails after launch, causing user complaints, hallucinations, and a feature rollback.
·
STAT
Work Lost
Six months of work can be lost if a feature is rolled back due to lack of proper evaluations.
▸ 5 Expand
CONC
Fundamental Nature of LLMs
Understanding how Large Language Models (LLMs) fundamentally work is crucial before building effective evaluations.
▸ 2 Expand
SUBC
LLMs Are Probabilistic
LLMs are statistical models that predict the next token based on probability distributions, not by 'knowing' facts.
·
EXMP
Capital of France Query
When asked 'What’s the capital of France?', an LLM predicts 'The capital of France is Paris' based on probability, not a lookup.
·
JUST
Different Outputs Explanation
The same prompt can yield different results because the model samples from a probability distribution, leading to varied outputs.
▸ 2 Expand
SUBC
The Temperature Problem
Temperature controls the randomness of LLM outputs, ranging from deterministic (Temperature = 0) to more random (Temperature = 1).
·
DETL
Typical Production Temperature
Most products use a temperature setting between 0.3 and 0.7 for LLMs.
·
INSG
Temperature Impact on Evals
Evaluations must be conducted at the same temperature setting used in production, or they become meaningless.
▸ 4 Expand
SUBC
The Context Window Problem
LLMs have limited context windows, and response quality degrades as the window fills, especially for information in the middle.
·
STAT
GPT-4 Context Window
GPT-4 has a context window of 128K tokens.
·
STAT
Claude Context Window
Claude has a context window of 200K tokens.
·
DETL
Lost in the Middle Problem
Critical information placed in the middle of a long prompt might be missed by the LLM, even within the context window.
·
INSG
Context Length Impact on Evals
Evaluations must test different context lengths, as prompt performance can vary significantly.
▸ 2 Expand
SUBC
The Prompt Sensitivity Problem
Small changes in prompts can lead to large and unpredictable changes in LLM outputs, making AI products fragile.
·
EXMP
Prompt Variation Example
Changing 'Please summarize this document' to 'Summarize this document' or adding 'Be concise' yields different results.
·
INSG
Prompt Variation Impact on Evals
Evaluations must test multiple prompt variations that users might actually type, not just one canonical prompt.
▸ 1 Expand
SUBC
The Hallucination Problem
LLMs can hallucinate, making up facts, citing non-existent sources, and inventing details because they predict plausible, not accurate, text.
·
INSG
Verification Required
It is essential to verify that LLM output is factually correct, not just that it 'looks good'.
▸ 5 Expand
CONC
Build Evaluation Rubric
Building an evaluation rubric is essential to define 'good' quality and measure the performance of AI features.
▸ 3 Expand
DETL
Start with User Scenarios
Begin rubric development by identifying the top 10 scenarios users will encounter with the AI feature.
·
EXMP
Chatbot Scenarios
For a customer support chatbot, scenarios include questions about return policy, shipping times, product specs, account issues, and unclear queries.
·
EXMP
Code Generation Tool Scenarios
For a code generation tool, scenarios include requests for simple functions, complex algorithms, refactoring, bug fixes, and tests.
·
INSG
User Scenarios as Test Cases
The identified user scenarios become the test cases for evaluating the AI feature.
▸ 2 Expand
DETL
Define Success Criteria
For each scenario, define specific, measurable, and unambiguous criteria for what constitutes a successful AI response.
·
EXMP
Good vs Bad Criteria
Bad criteria: 'The response is helpful'; Good criteria: 'The response contains the correct return window (30 days) and includes the return portal link'.
·
EXMP
Code Success Criteria
Good code success criteria: 'The code passes all test cases, follows project style guide, and includes error handling'.
▸ 2 Expand
DETL
Build Rubric Categories
A good rubric typically has 4-6 categories, each with a 1-5 scoring scale.
·
DETL
Rubric Categories
Key categories include Correctness, Completeness, Clarity, Tone, Safety, and Efficiency.
·
DETL
Scoring Scale Definition
The 1-5 scale ranges from 'Completely fails' (1) to 'Fully succeeds' (5), with clear definitions for each score.
▸ 2 Expand
DETL
Create Reference Examples
For each category and score level, create reference examples to establish ground truth for evaluators.
·
EXMP
Correctness Example Scores
Examples for 'Correctness' in a support chatbot demonstrate scores of 5 (full success), 3 (partial success), and 1 (factual error).
·
JUST
Reference Examples Purpose
These examples show human or LLM evaluators what specific quality levels look like.
▸ 1 Expand
DETL
Test Your Rubric
Have 2-3 people independently grade the same 10 outputs using the rubric and calculate inter-rater reliability to refine it.
·
JUST
Reliable Evals Need Good Rubric
A bad rubric leads to bad evals, while a good rubric ensures reliable evaluations.
▸ 5 Expand
CONC
Evaluation Metrics Framework
Different AI use cases require specific metrics for effective evaluation, as there is no 'one size fits all' approach.
▸ 2 Expand
SUBC
Retrieval Metrics
Metrics for AI systems that retrieve information, such as RAG systems, focus on relevance and surfacing the right documents.
·
DETL
Key Retrieval Metrics
Key metrics include Precision, Recall, F1 Score (harmonic mean), Mean Reciprocal Rank (MRR), and Normalized Discounted Cumulative Gain (NDCG).
·
INSG
Most Important Retrieval Metrics
For most AI Product Manager use cases, F1 and NDCG are the most important retrieval metrics.
▸ 2 Expand
SUBC
Generation Metrics
Metrics for AI systems that generate text, focusing on similarity and overlap with reference texts.
·
DETL
Text Generation Metrics
Metrics include BLEU (translation), ROUGE (summarization), METEOR (synonyms/stemming), and BERTScore (semantic similarity).
·
INSG
Most Robust Generation Metric
BERTScore is recommended as the most robust generation metric for most AI products.
▸ 3 Expand
SUBC
Task-Specific Metrics
Some AI tasks require custom metrics tailored to the specific requirements and desired outcomes of that task.
·
EXMP
Code Generation Metrics
Metrics for code generation include compilation success, test passage, style guide adherence, and cyclomatic complexity.
·
EXMP
Customer Support Metrics
Metrics for customer support include correct information, required links, brand tone matching, and issue resolution.
·
EXMP
Summarization Metrics
Metrics for summarization include capturing key points, omitting irrelevant details, length, and coherence.
▸ 2 Expand
SUBC
LLM-as-Judge Metrics
Using an LLM to grade outputs based on a rubric can correlate better with human judgment than traditional metrics.
·
DETL
LLM Judge Method
Ask an LLM (e.g., GPT-4 or Claude) to score a response on a 1-5 scale for helpfulness or other criteria.
·
JUST
Good Rubric is Key for LLM Judge
The effectiveness of an LLM judge relies on providing it with a well-defined rubric and reference examples.
▸ 1 Expand
DCSN
Choosing the Right Metrics
The choice of metrics depends on the AI's primary function, often requiring a combination of multiple metrics.
·
DETL
Metrics Decision Tree
Use Precision/Recall/F1 for document retrieval, BERTScore for generating text similar to references, task-specific metrics for specific tasks, and LLM-as-judge for holistic quality.
▸ 7 Expand
CONC
Build LLM Judges Step-by-Step
Implementing LLM judges involves a step-by-step process from defining evaluation prompts to continuous evaluation.
▸ 1 Expand
DETL
Step 1: Define Evaluation Prompt
The evaluation prompt for an LLM judge needs four components: the rubric, reference examples, input query, and output to evaluate.
·
EXMP
Evaluation Prompt Template
A template includes criteria like Correctness, Completeness, Clarity, and Tone with 1-5 scoring, reference examples, user query, and assistant response.
▸ 1 Expand
DETL
Step 2: Test on Known Examples
Manually test the LLM judge with 10 outputs that have known correct scores, comparing judge scores to ground truth and refining the prompt as needed.
·
DETL
Common Prompt Fixes
Common fixes include adding more reference examples, making criteria more specific, adding chain-of-thought reasoning, or using a better model (e.g., GPT-4 vs GPT-3.5).
▸ 2 Expand
DETL
Step 3: Implement with Claude Code
Automate the eval pipeline using tools like Claude Code by providing the evaluation prompt and test dataset to generate the necessary script.
·
EXMP
Claude Code Prompt
A prompt for Claude Code can request an eval pipeline taking a CSV, running through Claude as a judge, parsing scores, and outputting a summary CSV with averages and flagged low scores.
·
INSG
PM Role in Implementation
The PM's role is defining the rubric, curating test cases, and interpreting results, while implementation is largely automated.
▸ 2 Expand
DETL
Step 4: Read Results and Find Gaps
Generate a summary dashboard from eval results, focusing on mean scores, distributions, and worst/best performing examples to identify patterns.
·
EXMP
Claude Code Dashboard Request
Tell Claude Code to generate a dashboard showing mean score per criterion, score distribution, and top 10 worst/best performers.
·
INSG
Product Sense for Gaps
Product sense is crucial to interpret numbers, determine why failures occurred, and decide on corrective actions.
▸ 2 Expand
DCSN
Step 5: Set Quality Thresholds
Define minimum acceptable scores for each criterion and add them as pass/fail gates in the evaluation pipeline.
·
EXMP
Example Thresholds
Example thresholds include Correctness and Completeness ≥4.0 average, and Clarity and Tone ≥3.5 average.
·
EXMP
Claude Code Threshold Integration
Instruct Claude Code to add pass/fail checks to the eval pipeline, flagging runs if any criterion average drops below defined thresholds.
▸ 1 Expand
DETL
Step 6: Run Evals Continuously
Implement continuous evaluation before every release, daily in production, after prompt changes, and after model updates to catch regressions.
·
EXMP
Claude Code Continuous Eval Setup
Tell Claude Code to run the eval pipeline nightly against a 1% random sample of production traffic and send Slack notifications for threshold drops.
▸ 4 Expand
CONC
Common LLM Judge Pitfalls
Several common pitfalls can undermine the effectiveness of LLM judges if not addressed.
▸ 1 Expand
DETL
Pitfall 1: Same Model for Judge/Product
Using the same model as both the judge and the product can lead to biased evaluations.
·
DCSN
Solution: Stronger Judge Model
Use a stronger model as the judge (e.g., GPT-4 to judge GPT-3.5 outputs).
▸ 1 Expand
DETL
Pitfall 2: Not Calibrating Judge
Failing to calibrate the LLM judge regularly can result in unreliable scores.
·
DCSN
Solution: Calibrate Regularly
Regularly compare judge scores to human scores and adjust prompts to ensure accuracy.
▸ 1 Expand
DETL
Pitfall 3: Too Many Dimensions
Judging too many dimensions at once can overwhelm the LLM judge and reduce accuracy.
·
DCSN
Solution: Break Down Rubrics
Break complex rubrics into multiple judge calls to evaluate fewer dimensions per call.
▸ 1 Expand
DETL
Pitfall 4: Non-Zero Temperature
Using a temperature greater than 0 for LLM judges introduces randomness, making scores inconsistent.
·
DCSN
Solution: Use Temperature=0
Always use temperature=0 for LLM evaluations to ensure deterministic and consistent scores.
▸ 5 Expand
CONC
Production Monitoring That Works
Effective AI evaluation extends beyond launch with robust production monitoring to ensure ongoing performance and value.
▸ 4 Expand
SUBC
Three Layers of Monitoring
Production monitoring involves three layers: System Metrics, Quality Metrics, and Business Metrics.
▸ 1 Expand
DETL
Layer 1: System Metrics
These are basic health metrics indicating infrastructure issues.
·
DETL
Examples of System Metrics
System metrics include Latency (p50, p95, p99), Error rate, Token usage, API costs, and Timeout rate.
▸ 1 Expand
DETL
Layer 2: Quality Metrics
These measure the performance and quality of the AI's output.
·
DETL
Examples of Quality Metrics
Quality metrics include average LLM judge scores, human feedback scores (thumbs up/down), task success rate, and hallucination rate.
▸ 1 Expand
DETL
Layer 3: Business Metrics
These measure if the AI is delivering value and achieving business objectives.
·
DETL
Examples of Business Metrics
Business metrics include feature adoption rate, user retention, customer satisfaction (CSAT/NPS), support ticket deflection, and revenue impact.
·
INSG
All Layers Are Critical
All three layers are necessary; ignoring any layer leaves gaps in understanding AI performance and value.
▸ 1 Expand
DETL
Setting Up Automatic Alerts
Establish alerts for critical performance deviations to enable immediate investigation.
·
EXMP
Alert Criteria Examples
Alerts should be set for quality scores dropping below threshold, error rates spiking above 1%, p95 latency exceeding 3 seconds, or hallucination rate exceeding 5%.
▸ 2 Expand
DETL
The Human Review Queue
Sample 1% of production traffic daily for human review of 10-20 real user interactions using the rubric.
·
JUST
Human Review Catches Misses
Human review catches issues LLM judges miss and keeps the team connected to real user experiences.
·
DCSN
Recalibrate Judge if Divergent
If human scores diverge from LLM judge scores, recalibrate the LLM judge.
▸ 2 Expand
DETL
The Feedback Loop
Production monitoring findings should feed back into the evaluation dataset to continuously improve evals.
·
DETL
Feedback Loop Steps
Add bad production outputs to the test dataset, label them with correct scores, and rerun evals to ensure the system catches them.
·
INSG
Virtuous Cycle of Evals
This feedback loop creates a virtuous cycle, continuously improving the evaluation system over time.
▸ 1 Expand
DCSN
When to Rollback
Define clear rollback criteria beforehand to quickly reverse a feature if it fails in production.
·
EXMP
Rollback Criteria Examples
Criteria include quality score drop >10% from baseline, error rate exceeding 5%, more than 3 critical bugs in 24 hours, or negative business impact.
▸ 7 Expand
CONC
Final Words: Key Actions
AI evals combine product sense, technical understanding, and statistical thinking, forming a new essential PM skill.
·
DCSN
Build Eval Rubric
Build a comprehensive evaluation rubric specifically for your product.
·
DCSN
Implement Right Metrics
Implement appropriate metrics, including retrieval, generation, task-specific, and LLM-as-judge metrics.
·
DCSN
Create LLM Judge
Create an LLM judge to automate the evaluation process.
·
DCSN
Set Quality Thresholds
Establish clear quality thresholds before launching any AI feature.
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DCSN
Monitor Quality Continuously
Monitor AI quality continuously once it is in production.
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DCSN
Feedback Production Learnings
Integrate learnings from production back into your evaluation dataset for ongoing improvement.
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INSG
Do Not Ship Without Evals
It is crucial never to ship an AI product without a robust evaluation system in place.
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