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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Experiment Design for A/B LLM

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

Dataset Card Draft for LLM Training (Advanced)

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

A/B Testing Experiment Design

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

Dataset Card Draft for LLM Training

Specific guidelines for creating dataset cards for LLM training datasets.

Dataset Card Draft

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

ML Interview Evaluation Framework

A structured rubric for evaluating candidates in machine learning roles.

Contract Clause Extraction

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

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.

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.

Hybrid Search: BM25 + Dense Re-Ranking

This tutorial explores the integration of BM25 and dense re-ranking techniques to enhance search accuracy. Prerequisites include familiarity with information retrieval concepts and basic machine learning.

Canary Prompts for Regression Detection

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

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.

Hybrid Search: BM25 + Dense Re-Ranking for Academic Research

This tutorial explores the integration of BM25 and dense re-ranking for enhancing academic search engines. Familiarity with information retrieval concepts is required.

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.

Human-in-the-Loop for High-Stakes Actions

Integrating human oversight in automated systems to ensure accuracy and accountability in critical scenarios. Prerequisites include understanding of automation frameworks and risk management principles.

Golden-Set Design for RAG Faithfulness

Understand how to design a golden set for evaluating the faithfulness of Retrieval-Augmented Generation (RAG) models. Prerequisites include familiarity with RAG systems and evaluation metrics.

Metadata Filters and ACL-aware Retrieval

Explore how to implement metadata filters and Access Control List (ACL)-aware retrieval in your applications. Prerequisites include knowledge of metadata management and ACL concepts.

Implementing SLI/SLO for Generative Endpoints

This tutorial outlines how to define and implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) for generative endpoints, ensuring high availability and performance. Prerequisites include understanding of SLIs, SLOs, and basic API concepts.

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.

fine-tuning

The process of adjusting a pre-trained model on a new, often smaller dataset to improve performance on a specific task.

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quantum-machine-learning

An interdisciplinary approach merging quantum computing with machine learning techniques.

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bayesian-optimization

A probabilistic model-based optimization technique for finding the minimum of a function.

adaptive-learning

A method where the system optimizes its learning process based on user interactions and performance.

model-compression

Techniques for reducing the size and complexity of machine learning models while maintaining performance.

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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LlamaIndex

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

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LangChain

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

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.

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.