🎯 How to Make Your GenAI App More Relevant: Measure, Test & Improve with Langflow

Struggling to get your GenAI or RAG application into production? You’re not alone—and we’ve got the tools to help.

In this video, Adarsh (Solution Engineer, DataStax) walks through how to evaluate and improve GenAI applications using an automated toolkit built to measure precision and other accuracy metrics. Learn how to generate ground truth datasets, run retrieval accuracy tests, and fine-tune your system to hit 95%+ relevance—all in-browser.

✅ No more guesswork
✅ No more manual evaluation
✅ Just measurable results—and better outcomes.

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What You’ll Learn:
📊 Why evaluating GenAI apps is critical
📄 How to auto-generate question-answer (QA) pairs from your own data
⚙️ How to use the Testing RAG Toolkit to assess performance
📈 Key metrics to track: precision, recall, hallucination, faithfulness, and more
📦 How to store ground truth data in Astra DB for reusability and scale
🧪 How to integrate with Langflow to debug, test, and improve quickly

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Demo Highlights:
🔹 Generate semantic chunks from PDFs using Google Flash
🔹 Auto-create ground truth datasets (CSV + AstraDB)
🔹 Evaluate accuracy against ground truth using built-in metrics
🔹 Visualize performance in a simple browser-based dashboard
🔹 Iterate your way to production-ready with LLM-powered tools

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Core Evaluation Metrics:
1. Precision - Measures how many of the retrieved documents are actually relevant to the query.
(Formula: Relevant Retrieved Docs / Total Retrieved Docs)
2. Recall - Measures how many of the relevant documents were actually retrieved.
(Formula: Relevant Retrieved Docs / Total Relevant Docs)
3. F1 Score - Harmonic mean of precision and recall. It balances both metrics into a single score.
(Formula: 2 * (Precision * Recall) / (Precision + Recall) )

Generation-Focused Metrics:
4. Claim Recall - Measures how many factual claims made in the answer are supported by retrieved documents. High value indicates fewer hallucinations.
5. Context Precision - Measures how much of the content used in the generated answer actually comes from relevant retrieved contexts. Think of it as: “Is the model using the right retrieved content when answering?”
6. Context Utilization -Fraction of retrieved relevant documents that were actually used in generating the answer. Highlights efficiency of retrieval usage.

Noise Sensitivity Metrics:
7. Noise Sensitivity (Relevant) - Measures how much adding irrelevant documents affects the answer quality when relevant docs are present. Low sensitivity = model is robust even if noise is added.
8. Noise Sensitivity (Irrelevant) - Measures how much adding irrelevant documents affects the answer when only irrelevant docs are retrieved. Helps check hallucination risk under full noise.

Trustworthiness & Truthfulness:
9. Self-Knowledge - How well the model abstains from answering when it doesn’t know or lacks relevant information. Good models admit ignorance rather than hallucinating.
10. Faithfulness - Measures whether the generated answer strictly aligns with the retrieved evidence. High faithfulness = no added or made-up info.
11. Hallucination - Measures how much of the generated answer is not supported by any retrieved document. High hallucination = more made-up content.

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🔗 Download the Framework at:
https://github.com/shiragannavar/Test...
shiragannavar/Testing-RAG
Try Astra DB: https://astra.datastax.com
Docs: https://docs.datastax.com

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Let’s build GenAI apps that are accurate, reliable, and production-ready!
#GenAI #Langflow #RAG #AIevaluation #AgenticAI #AstraDB #LLM #AItools #GroundTruth #AIworkflow