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AI Cheat Sheets

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What is ROUGE Metrics – Recall-Oriented Understudy for Gisting Evaluation?

2025-07-08T11:24:28+00:00

Recall-Oriented Understudy for Gisting Evaluation (ROUGE) Cheat Sheet ROUGE is a family of metrics designed to assess the similarity between machine-generated text (candidate) and human-written reference text (ground truth) in NLP tasks like text summarization and machine translation. Measures how well generated text captures key information and structure from reference text, emphasizing recall (proportion of relevant information preserved). Score Range: 0 to 1, where higher scores indicate greater similarity between candidate and reference texts. Key Use Cases: Evaluating text summarization systems. Assessing machine translation quality. Analyzing content accuracy in generated text. Types of ROUGE Metrics ROUGE-N: Measures the overlap of [...]

What is ROUGE Metrics – Recall-Oriented Understudy for Gisting Evaluation?2025-07-08T11:24:28+00:00

What is BERTScore – Bidirectional Encoder Representations from Transformers Score?

2025-07-03T06:03:55+00:00

BERTScore (Bidirectional Encoder Representations from Transformers Score) Cheat Sheet BERTScore is an effective evaluation metric that looks beyond surface-level word matching to assess the meaning behind the generated text. Instead of counting overlapping words like traditional metrics such as BLEU or ROUGE, BERTScore taps into the power of pre-trained transformer models (like BERT) to compare the semantic similarity between tokens in the generated output and a reference sentence. It does this by calculating the cosine similarity between their contextual embeddings. Initially proposed by Zhang et al. (2020), BERTScore has quickly become a popular choice in natural language processing tasks where [...]

What is BERTScore – Bidirectional Encoder Representations from Transformers Score?2025-07-03T06:03:55+00:00

Azure AI Bot Service

2025-07-01T08:07:59+00:00

Azure AI Bot Service Cheat Sheet A managed platform for building, deploying, and managing intelligent chatbots with conversational AI across multiple channels. Enables developers to create scalable, AI-powered bots using natural language processing (NLP) and low-code tools. Key Components: Azure Bot Service: Manages hosting, scaling, and monitoring of bots. Bot Framework SDK: Offers development tools for bot creation (C#, JavaScript, Python). Microsoft Copilot Studio: Low-code platform for building bots with a visual interface. Key Features Multi-Channel Support: Connects to Microsoft Teams, Slack, Web Chat, email, and SMS. Natural Language Processing: Integrates with Azure AI Language for intent recognition and knowledge [...]

Azure AI Bot Service2025-07-01T08:07:59+00:00

What is Retrieval Augmented Generation (RAG) in Machine Learning?

2025-06-30T03:46:57+00:00

Retrieval-Augmented Generation (RAG) Cheat Sheet Retrieval-Augmented Generation (RAG) is a method that enhances large language models (LLMs) outputs by incorporating information from external, authoritative knowledge sources. Instead of relying solely on pre-trained data, RAG retrieves relevant content at inference time to ground its responses. LLMs (Large Language Models) are trained on massive datasets and use billions of parameters to perform tasks like: Question answering Language translation Text completion RAG extends LLM capabilities to domain-specific or private organizational knowledge without requiring model retraining. It provides a cost-efficient way to improve the relevance, accuracy, and utility of LLM outputs in dynamic or [...]

What is Retrieval Augmented Generation (RAG) in Machine Learning?2025-06-30T03:46:57+00:00

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