Experiment Design for A/B LLM - Advanced

In-depth guide for designing A/B tests specifically for large language models.

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Technical Workshop Lesson Plan

An organized lesson plan template for conducting technical workshops on LLMs and their applications.

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Dataset Card Draft for LLM Training (Advanced)

An advanced template for creating detailed dataset cards focusing on comprehensive metadata for LLM training datasets.

Dataset Card Draft for LLM Training

Specific guidelines for creating dataset cards for LLM training datasets.

Experiment Design for A/B LLM

A structured approach to designing experiments for A/B testing in language models.

Dataset Card Draft

A standardized template for documenting dataset characteristics, usage, and limitations for LLM training.

A/B Testing Experiment Design

A structured template to design A/B tests for LLM applications, ensuring consistency in experiment setup.

ML Interview Evaluation Framework

A structured rubric for evaluating candidates in machine learning roles.

Localization Glossary Review (Duplicate)

A duplicate of the previous glossary review entry, focusing on terminology consistency.

HR Policy Q&A Framework with Citations

A framework for generating HR policy-related questions and answers with references to legal statutes or company guidelines.

ML Role Interview Rubric

A structured rubric designed for evaluating candidates in machine learning roles.

Contract Clause Extraction

Extract and summarize key clauses from legal contracts for easier review.

Ollama

Local model runtime for running and serving open LLMs on developer machines and private infrastructure, with simple pull/run workflows and API access.

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LangChain

Application framework for orchestrating LLM workflows, tool calling, retrieval, and agents across multiple providers in Python and TypeScript ecosystems.

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LlamaIndex

Data framework for LLM applications focused on ingestion pipelines, indexing, retrieval, and query orchestration over private and enterprise content sources.

OpenAI Playground

Provider of widely used frontier model APIs for text, vision, and audio, with strong developer tooling and broad ecosystem adoption across production AI applications.

LangGraph

LangGraph is a library for building stateful, cyclic agent and workflow graphs on top of LangChain—suited to multi-step tools, human-in-the-loop approvals, and durable execution patterns that go beyond linear chains.

Groq

GroqCloud offers very low-latency, high-throughput LLM inference using Groq’s LPU-style hardware, with OpenAI-compatible APIs for select open and partner models aimed at interactive and batch production workloads.

Hugging Face

Hub for open models, datasets, and Spaces demos, plus Inference Endpoints, Transformers, and enterprise features for teams that train, fine-tune, or serve open-weight and partner models at scale.

Observability: Traces for LLM + Tool Spans

Implementing observability practices to trace interactions between large language models (LLMs) and external tools. Prerequisites include knowledge of observability tools and LLM architectures.

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Metadata Filters and ACL-Aware Retrieval in Legal Document Management

This tutorial outlines the implementation of metadata filters and Access Control List (ACL)-aware retrieval systems in legal document management applications. Prerequisites include knowledge of legal data structures and basic programming skills.

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Enhancing Observability with Traces for LLM and Tool Spans in Data Pipelines

This tutorial focuses on enhancing observability in data pipelines that utilize large language models (LLMs) by implementing tracing for both LLM and tool spans. Prerequisites include familiarity with observability concepts and experience with LLMs.

Multimodal Prompts for Document QA in Legal Settings

Using multimodal prompts can improve document question answering (QA) in legal contexts. Prerequisites include access to relevant legal documents and a model capable of processing multimodal inputs.

Chunking Strategies for Legal PDFs: Improving Document Retrieval

This tutorial focuses on optimizing chunking strategies for legal documents to enhance retrieval accuracy. Prerequisites include familiarity with document processing and retrieval systems.

SLI/SLO for Generative Endpoints

Establishing Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for generative endpoints is crucial for maintaining quality and reliability. This tutorial outlines how to define and implement SLIs/SLOs effectively.

Canary Prompts for Regression Detection

Utilizing canary prompts to detect regressions in language models. Prerequisites include familiarity with regression testing and LLM evaluation metrics.

Chunking Strategies for Legal/Medical PDFs

Learn effective chunking strategies for processing legal and medical PDFs to enhance information retrieval. Prerequisites include familiarity with PDF processing and natural language processing concepts.

Ensuring PII Handling in RAG Pipelines for Legal Firms

This tutorial focuses on best practices for handling Personally Identifiable Information (PII) in RAG pipelines within legal firms. It requires knowledge of legal compliance and data protection standards.

LLaMA 3 70B

LLaMA 3 70B features 70 billion parameters and a context window of 32k tokens, optimized for high-performance text generation and understanding across diverse tasks.

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LLaMA 3 8B

LLaMA 3 8B is a compact model with 8 billion parameters, designed for efficient text generation and understanding with a context window of 8k tokens.

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Mistral Large

Mistral Large supports up to 16k tokens with a response latency of 150ms, targeting enterprise-level applications and complex document understanding.

Claude 3 Opus

Claude 3 Opus enhances AI's conversational abilities with a broader understanding of context and intent, featuring a context window of 16k tokens for improved engagement in dialogues.

Gemini Flash

Gemini Flash focuses on fast inference with a 4k token limit, ideal for applications requiring quick responses while maintaining decent accuracy in language tasks.

distributed-learning

A machine learning paradigm where the training data is distributed across multiple devices or nodes.

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quantum-machine-learning

An interdisciplinary approach merging quantum computing with machine learning techniques.

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few-shot-learning

A machine learning paradigm that trains models with very few labeled examples.