The foresight library within fore SDK allows you to easily evaluate the performance of your LLM system based on a variety of metrics.

You can try out foresight for free at https://foresight.foreai.co.

1. Install the package using npm:

   npm install fore-ai

2. • Get started with the following lines:

   const { Foresight } = require("fore-ai");
   
   const foresight = new Foresight({ apiToken: "<YOUR_API_TOKEN>" });
   
   await foresight.log({
   	query: "What is the easiest programming language?",
   	response: "Python",
   	contexts: ["Python rated the easiest programming language"],
   	tag: "my_awesome_experiment",
   });
   
   // You can add more such queries using foresight.log
   // ....
   
   await foresight.flush();

   • Or alternatively to curate your evalsets and run regular evals against them do:

   const { Foresight, MetricType } = require("fore-ai");
   
   const foresight = new Foresight({ apiToken: "<YOUR_API_TOKEN>" });
   
   const evalset = await foresight.createSimpleEvalset({
   	evalsetId: "programming-languages",
   	queries: [
   		"hardest programming language?",
   		"easiest programming language?",
   	],
   	referenceAnswers: ["Malbolge", "Python"],
   });
   
   const runConfig = {
   	evalsetId: "programming-languages",
   	experimentId: "my-smart-llm",
   	metrics: [MetricType.GROUNDEDNESS, MetricType.REFERENCE_FACT_RECALL],
   };
   
   const myGenerateGn = (query) => {
   	// Do the LLM processing with your model...
   	// Here is some demo code:
   
   	return {
   		generatedResponse: query.includes("hardest")
   			? "Malbolge"
   			: "Python",
   		contexts: [
   			"Malbolge is the hardest language",
   			"Python is the easiest language",
   		],
   	};
   };
   
   await foresight.generateAnswersAndRunEval({
   	generateFn: myGenerateGn,
   	runConfig,
   });

Depends on:

• LLM's generated response;
• Context used for generating the answer.

The metric answers the question: Is the response based on the context and nothing else?

This metric estimates the fraction of facts in the generated response that can be found in the provided context.

Example:

• Context: The front door code has been changed from 1234 to 7945 due to security reasons.
• Q: What is the current front door code?
• A1: 7945. [groundedness score = 0.9]
• A2: 0000. [groundedness score = 0.0]
• A3: 1234. [groundedness score = 0.04]

Depends on:

• A user query;
• An LLM's generated response to be evaluated;
• A reference response to compare the generated response with.

The metric answers the question: How many facts from the reference answer does the candidate answer mention?

This metric checks that the answer given by the LLM is mentioning all the facts listed in the reference answer. Additional information is not penalised.

Example:

• Question: Give me a checklist to prepare for my hiking trip to the mountains.
• Reference response: You should bring your a water bottle, hiking shoes and sunscreen.
• Candidate answer 1: Here is a list of items to bring: 1) hiking shoes; 2) a water bottle. [reference fact recall score = 0.67]
• Candidate answer 2: Here is a list of items to bring: 1) backpack with food; 2) hiking shoes; 3) a water bottle.[reference fact recall score = 0.67]
• Candidate answer 3: Here is a list of items to bring: 1) backpack with food; 2) hiking shoes; 3) a water bottle; 4) sunscreen.[reference fact recall score = 1.0]