Evaluators & Evaluations: Benchmarking AI/agent Performance

Evaluators in InnoSynth-Forjinn are core components for measuring, comparing, and ensuring the quality of AI-generated responses, agent actions, and chatflow outputs. They bring scientific rigor and automation to the process of model evaluation, prompt tuning, and workflow improvement.

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What is an Evaluator?

An Evaluator is a module (node or workflow) that compares a system's output (from an LLM, agent, or workflow) against a known ground truth, using a variety of metrics:

• Exact match (string/equality)
• F1/ROUGE/BLEU (for text similarity)
• Semantic similarity (embeddings-based)
• Human preference/annotation
• Custom scoring (via code or external API)

Evaluators can run automatically, in batch (across whole datasets), or interactively in the UI.

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Types of Evaluations

• Automated Evaluations: Run against a dataset, compare predictions to annotated ground truths, compute accuracy, precision, recall, etc.
• Manual Evaluations: Human reviewers approve, reject, or score specific outputs using platform UI tools.
• Hybrid: Automated filter, then human review for borderline or flagged cases.
• Second-Order Analysis: Use one LLM/agent to critique or grade another's output ("self-evaluation", GPT-judge, etc).

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Setting Up Evaluators

1. Add an Evaluator Node

• Drag an Evaluator node (e.g. ExactMatchEvaluator, FuzzyEvaluator, LLMScorer) to your workflow.
• Connect it after the node whose output you want to assess (typically LLM, Retriever, or Chat Output).
• Attach your dataset (with ground truths) in the node config.

2. Configure Metrics & Criteria

• Choose or define the metric(s): e.g.,
  • String match: Path to actual output, path to ground truth
  • Semantic: Use set model for embedding similarity
  • Custom: Supply Python/JS code to calculate match/score

3. (Optional) UI & Human-in-the-Loop

• Enable manual evaluation mode for human reviewers
• Use annotation UI to accept/reject, rate, or provide feedback on model outputs

4. Run Evaluation

• Manually test on sample inputs
• Or, run batch evaluation against all records in a dataset
• Results are logged per sample and summarized (scores, confusion matrices, histograms, etc.)

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Example: Evaluating RAG QA with String & Semantic Scores

• Upload Dataset: Q/A pairs (question, context, answer)
• Configure Workflow: Retriever → LLM → Evaluator
• Set Evaluator:
  • Metric: ‘semantic similarity’ with threshold 0.85
• Run: On each record, the Evaluator compares model output to the known answer, scores, and logs result.
• Visualize: Charts show per-metric performance and failed cases.

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Results & Reporting

• Summaries: Overall accuracy, F1, semantic match
• Per-sample reports for error analysis
• Export to CSV/JSON for deeper/reproducible analysis

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Troubleshooting

• Scores lower than expected: Check for case, whitespace, or format differences; semantic metrics are more tolerant than string metrics.
• Dataset not recognized: Ensure column mapping is correct and all required fields are present.
• Stuck on "Evaluating..." Large datasets can take time; batch in smaller chunks or optimize retriever/LLM speed.

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Best Practices

• Always validate your evaluation set is reflective of live data.
• Avoid leaking answer/context in prompts used for evaluation.
• Use both automated and human/manual review for high-stakes or critical tasks.
• Document metric settings and rationale in Sticky Notes or metadata.
• Version your datasets and flows for reproducibility.

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Evaluators make your AI measurable, improvable, and trustworthy—turning black box AI into repeatable, scientific-grade workflows.