20 - Voice of Industry Experts - The Ultimate Guide to Gen AI Evaluation Metrics Part 2
The Ultimate Guide to Gen AI Evaluation Metrics
In our last post, we unpacked the big picture of Gen AI evaluation — why it matters and the different ways we can look at it. But let’s be honest: knowing that evaluation is important is only half the story. The real challenge is figuring out which metrics actually tell you something useful about your model.
That’s where this post comes in. We’re zooming in on the core evaluation metrics every AI practitioner should have in their toolkit. Think of it as your go-to playbook: from the basics like accuracy and precision, to deeper measures such as recall, F1 score, and contextual relevance, all the way to metrics designed for generative AI outputs.
By the end, you won’t just recognize these terms — you’ll know when and why to use them, plus how they play out in real-world scenarios like spam detection, search engines, or even evaluating AI-generated answers.
If you’ve ever looked at a model’s performance report and thought, “Okay, but what does this number really mean?” — this post is for you.
Classification Metrics
In classification tasks, we use below key
metrics:
· Understanding Accuracy Metrics
Accuracy measures the proportion of
correctly classified instances out of all instances in the dataset.
How Accuracy Works
Accuracy evaluates the performance of a
model by comparing the predicted output to the actual output.
Key Considerations
-
Correct Classification:
Proportion of instances that are correctly classified by the model.
-
Error Rate: Proportion of
instances that are misclassified by the model.
Example: Suppose a model is used to predict
customer churn for a telecom company like AT&T or Verizon. The model
correctly predicts 900 out of 1000 customers who will either stay or leave the
company
Accuracy Scores can be interpreted as
follows:
-
High: Higher accuracy indicates
better performance, suggesting that the model correctly predicts a large
proportion of customers who will stay or leave.
-
Low: Indicates lower accuracy,
suggesting potential issues with the model's performance.
By using Accuracy metrics, developers can
evaluate and improve the performance of their medical diagnosis models.
· Understanding Precision Metrics
Precision measures the proportion of true
positives among all positive predictions made by the model.
How Precision Works
Precision evaluates the performance of a
model by comparing the number of true positives to the total number of positive
predictions.
Key Considerations
-
True Positives: Proportion of
positive instances that are correctly classified by the model.
-
False Positives: Proportion of
negative instances that are misclassified as positive by the model.
Example:Suppose a spam filter model is used
to detect spam emails. Out of 100 emails predicted as spam, 90 are actually
spam (true positives) and 10 are not spam (false positives).
Precision Scores can be interpreted as
follows:
-
High: Higher precision
indicates better performance, suggesting that the model correctly classifies a
large proportion of spam emails.
-
Low: Indicates lower precision,
suggesting potential issues with the model's performance.
By using Precision metrics, developers can
evaluate and improve the performance of their spam filter models.
Understanding Recall Metrics
Recall measures the proportion of true
positives among all actual positive instances in the dataset.
How Recall Works
Recall evaluates the performance of a model
by comparing the number of true positives to the total number of actual
positive instances.
Key Considerations
-
True Positives: Proportion of
positive instances that are correctly classified by the model.
-
False Negatives: Proportion of
positive instances that are misclassified as negative by the model.
Example :Suppose a facial recognition model
is used to identify individuals in a crowd. Out of 100 individuals who are
actually present, the model correctly identifies 80 (true positives) and misses
20 (false negatives).
Recall Scores can be interpreted as
follows:
-
High: Higher recall indicates
better performance, suggesting that the model correctly identifies a large
proportion of actual positive instances.
-
Low: Indicates lower recall,
suggesting potential issues with the model's performance.
By using Recall metrics, developers can
evaluate and improve the performance of their facial recognition models.
· Understanding F1 Score Metrics
F1 Score is the harmonic mean of precision
and recall, providing a balanced measure of both.
How F1 Score Works
F1 Score evaluates the performance of a
model by comparing the precision and recall scores.
Key Considerations
-
Balanced Measure: F1 Score
provides a balanced measure of precision and recall.
-
Harmonic Mean: F1 Score is
calculated as the harmonic mean of precision and recall.
Example: Suppose a sentiment analysis model
has a precision score of 0.85 and a recall score of 0.8 for detecting positive
sentiments on social media.
F1 Score can be interpreted as follows:
-
- High: Higher F1 Score
indicates better performance, suggesting a good balance between precision and
recall.
-
- Low: Indicates lower F1
Score, suggesting potential issues with the model's performance.
By using F1 Score metrics, developers can
evaluate and improve the performance of their sentiment analysis models.
Evaluating the Whole AI System
When assessing the entire AI system, we
consider metrics like:
-
Cost metrics: resources, time,
and performance
-
Direct Value: tangible
benefits, such as increased revenue or efficiency
-
Indirect Value: intangible
benefits, like improved customer satisfaction
Contextual Metrics
In Gen AI, context is crucial. We use
metrics like:
· Understanding Context Precision Metrics
Context Precision assesses the ranking
quality of retrieved content.
How Context Precision Works
Context Precision evaluates whether
relevant chunks are ranked higher than irrelevant ones in the retrieved
content.
Key Considerations
-
Ranking Quality: Context
Precision measures the proportion of relevant content that is ranked higher
than irrelevant content.
-
Relevant Content: Context
Precision assesses whether the most relevant content is presented first.
Example : Suppose a search engine retrieves
a list of documents related to a user's query. Context Precision would evaluate
whether the most relevant documents are ranked higher in the list.
Context Precision Scores can be interpreted
as follows:
-
High: Higher Context Precision
indicates better ranking quality, suggesting that relevant content is presented
first.
-
Low: Indicates lower Context
Precision, suggesting potential issues with the ranking algorithm.
By using Context Precision metrics,
developers can evaluate and improve the ranking quality of their search engines
or retrieval systems.
· Understanding Context Relevance Metrics
Context Relevance measures the relevancy of
retrieved content.
How Context Relevance Works
Context Relevance evaluates the proportion
of retrieved content that is relevant to the user's query or task.
Key Considerations
-
Relevant Content: Context Relevance measures
the proportion of retrieved content that is relevant to the user's query or
task.
-
Irrelevant Content: Context
Relevance assesses the proportion of retrieved content that is not relevant to
the user's query or task.
Example: Suppose a user searches for
information on "artificial intelligence". Context Relevance would
evaluate whether the retrieved content is actually related to artificial
intelligence.
Context Relevance Scores can be interpreted
as follows:
-
High: Higher Context Relevance
indicates better relevancy, suggesting that the retrieved content is highly
relevant to the user's query.
-
Low: Indicates lower Context
Relevance, suggesting potential issues with the retrieval algorithms
By using Context Relevance metrics,
developers can evaluate and improve the relevancy of their retrieval systems.
· Understanding Context Recall
Metrics
Context Recall measures the extent to which
all relevant entities and information are retrieved.
How Context Recall Works
Context Recall evaluates the proportion of
relevant entities and information that are retrieved by the system.
Key Considerations
-
Comprehensive Retrieval:
Context Recall measures the proportion of relevant entities and information
that are retrieved.
-
Missing Information: Context
Recall assesses the proportion of relevant entities and information that are
not retrieved.
Example: Suppose a user searches for
information on "companies in the tech industry". Context Recall would
evaluate whether the system retrieves all relevant companies in the tech
industry.
Context Recall Scores can be interpreted as
follows:
-
High: Higher Context Recall
indicates better comprehensive retrieval, suggesting that the system retrieves
most relevant entities and information.
-
Low: Indicates lower Context
Recall, suggesting potential issues with the retrieval algorithm.
By using Context Recall metrics, developers
can evaluate and improve the comprehensiveness of their retrieval systems.
Generation-Related Metrics
When evaluating generated responses, we
focus on metrics like:
Here are the metrics explained in a more
readable format:
· Understanding Faithfulness Metrics
Faithfulness measures the factual accuracy
of the generated answer.
How Faithfulness Works
Faithfulness evaluates whether the
generated answer is factually accurate and consistent with the available
information.
Key Considerations
-
Factual Accuracy: Faithfulness
measures the proportion of factually accurate information in the generated
answer.
-
Hallucinations: Faithfulness
assesses the presence of hallucinations or fabricated information in the
generated answer.
Example:Suppose a question answering system
generates an answer to the question "What is the capital of France?".
Faithfulness would evaluate whether the answer "Paris" is factually
accurate.
Faithfulness Scores can be interpreted as
follows:
-
High: Higher Faithfulness
indicates better factual accuracy, suggesting that the generated answer is
reliable and trustworthy.
-
Low: Indicates lower
Faithfulness, suggesting potential issues with the accuracy of the generated
answer.
By using Faithfulness metrics, developers
can evaluate and improve the factual accuracy of their question answering
systems.
· Understanding Answer Relevancy Metrics
Answer Relevancy assesses how well the
generated response answers the initial question.
How Answer Relevancy Works
Answer Relevancy evaluates whether the
generated response is relevant to the user's question and provides useful
information.
Key Considerations
-
Relevance: Answer Relevancy
measures the proportion of relevant information in the generated response.
-
Off-Topic Content: Answer
Relevancy assesses the presence of off-topic content in the generated response.
Example
Suppose a user asks "What are the
benefits of meditation?" and the system generates a response about the
history of meditation. Answer Relevancy would evaluate whether the response is
relevant to the user's question.
Answer Relevancy Scores can be interpreted
as follows:
-
High: Higher Answer Relevancy
indicates better relevance, suggesting that the generated response is useful
and answers the user's question.
-
Low: Indicates lower Answer
Relevancy, suggesting potential issues with the relevance of the generated
response.
By using Answer Relevancy metrics,
developers can evaluate and improve the relevance of their question answering
systems.
· Understanding Answer Correctness Metrics
Answer Correctness measures the accuracy of
the generated answer compared to the ground truth.
How Answer Correctness Works
Answer Correctness evaluates whether the
generated answer matches the ground truth or the expected answer.
Key Considerations
-
Accuracy: Answer Correctness
measures the proportion of accurate information in the generated answer
compared to the ground truth.
-
Errors: Answer Correctness
assesses the presence of errors in the generated answer.
Example: Suppose a question answering system
generates an answer to the question "What is the boiling point of
water?". Answer Correctness would evaluate whether the answer
"100°C" matches the ground truth.
Answer Correctness Scores can be
interpreted as follows:
-
High: Higher Answer Correctness
indicates better accuracy, suggesting that the generated answer is correct and
matches the ground truth.
-
Low: Indicates lower Answer
Correctness, suggesting potential issues with the accuracy of the generated
answer.
By using Answer Correctness metrics,
developers can evaluate and improve the accuracy of their question answering
systems.
- Fraud & Risk Management led with 53% of the vote
- Followed by Innovation & Future-Readiness at 28%
- Then Internal Operations Automation (13%)
- And Compliance & Governance (6%)
Based on this, I am excited to launch a new series focused on Fraud & Risk Management—exploring strategies, best practices, and emerging technologies that help organizations stay resilient in an increasingly complex landscape.
Stay tuned as we dive into insights, real-world case studies, and expert perspectives designed to strengthen your roadmap in this critical space.
Anshul Kala is results-driven AI and Data Solutions leader with extensive expertise in leveraging data analytics and artificial intelligence to drive business growth and innovation. She delivered high-impact projects and led cross-functional teams, and unlocked data-driven insights and solutions that transform businesses. She is passionate about harnessing the power of data and AI to enable informed strategic decisions and drive organizational success through data-driven innovation.
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