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Research and guides for building safe and reliable AI products. Helping thousands of AI engineers build safer products.

How a $5B Insurance Provider Uses LLMs for Risk Underwriting
AthinaEvaluationPrompt Engineering
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How a $5B Insurance Provider Uses LLMs for Risk Underwriting

Building an Ideal Tech Stack for LLM Applications From Scratch
RAGEvaluationTutorial
•

Building an Ideal Tech Stack for LLM Applications From Scratch

How E-com Giant Meesho Accelerated LLM Development Using Athina AI
Case StudyAthinaEvaluation
•

How E-com Giant Meesho Accelerated LLM Development Using Athina AI

5 Advanced Prompting Techniques to Improve Your LLM App’s Responses
AthinaPrompt Engineering
•

5 Advanced Prompting Techniques to Improve Your LLM App’s Responses

LLM Critics Help Catch LLM Bugs
Research PaperEvaluation
•

LLM Critics Help Catch LLM Bugs

Introducing Athina Prompt Management: A Powerful and Flexible Prompt Playground and CMS
Athina
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Introducing Athina Prompt Management: A Powerful and Flexible Prompt Playground and CMS

A Practical Guide to Measure and Improve Retrieval in a RAG-based LLM Application
AthinaRAG
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A Practical Guide to Measure and Improve Retrieval in a RAG-based LLM Application

Prompt Injection: Different Attacks and Defensive Techniques
SafetyPrompt Engineering
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Prompt Injection: Different Attacks and Defensive Techniques

Cookbook: How to set up Langchain tracing on Athina in 2 minutes
TutorialAthinaLangchain
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Cookbook: How to set up Langchain tracing on Athina in 2 minutes

From Noise to Clarity: Unraveling the Adversarial Suffix of Large Language Model Attacks via Translation of Text Embeddings
SafetyResearch Paper
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From Noise to Clarity: Unraveling the Adversarial Suffix of Large Language Model Attacks via Translation of Text Embeddings

Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification
SafetyHallucinationsResearch Paper
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Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification

The EVER (Real-Time Verification and Rectification) framework is designed to dynamically mitigate hallucinations during text generation by ensuring the accuracy and trustworthiness of each sentence before proceeding.

Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models
SafetyResearch Paper
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Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models

How to evaluate your Llama Index query engine using Ragas evals + Athina AI
EvaluationAthina
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How to evaluate your Llama Index query engine using Ragas evals + Athina AI

If you're using Llama Index to work with advanced retrieval strategies, you're going to need a great evaluation setup. Here's how you can use Athina's SDK to run Ragas evals on your Llama Index RAG pipeline.

Detect LLM Hallucinations in CI / CD: Evaluate your RAG pipeline using GitHub Actions + Athina / Ragas
SafetyAthinaEvaluation
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Detect LLM Hallucinations in CI / CD: Evaluate your RAG pipeline using GitHub Actions + Athina / Ragas

10 Rules for LLM Evaluation: What we learned after a year of building
EvaluationAthina
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10 Rules for LLM Evaluation: What we learned after a year of building

Text Summarization: LLM Failure Cases and Detection Methods
Evaluation
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Text Summarization: LLM Failure Cases and Detection Methods

Reflexion: Language Agents with Verbal Reinforcement Learning
Research PaperPrompt Engineering
•

Reflexion: Language Agents with Verbal Reinforcement Learning

How a $5B Insurance Provider Uses LLMs for Risk Underwriting
AthinaEvaluationPrompt Engineering
•

How a $5B Insurance Provider Uses LLMs for Risk Underwriting

Building an Ideal Tech Stack for LLM Applications From Scratch
RAGEvaluationTutorial
•

Building an Ideal Tech Stack for LLM Applications From Scratch

How E-com Giant Meesho Accelerated LLM Development Using Athina AI
Case StudyAthinaEvaluation
•

How E-com Giant Meesho Accelerated LLM Development Using Athina AI

5 Advanced Prompting Techniques to Improve Your LLM App’s Responses
AthinaPrompt Engineering
•

5 Advanced Prompting Techniques to Improve Your LLM App’s Responses

LLM Critics Help Catch LLM Bugs
Research PaperEvaluation
•

LLM Critics Help Catch LLM Bugs

Introducing Athina Prompt Management: A Powerful and Flexible Prompt Playground and CMS
Athina
•

Introducing Athina Prompt Management: A Powerful and Flexible Prompt Playground and CMS

LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
Research PaperPrompt Engineering
•

LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition

Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models
Research PaperPrompt Engineering
•

Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models

A Practical Guide to Measure and Improve Retrieval in a RAG-based LLM Application
AthinaRAG
•

A Practical Guide to Measure and Improve Retrieval in a RAG-based LLM Application