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Model API Cons

term

Data leakage, no control over training data, lack of access to logprobs, rate limits, and no transparency in model changes

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a

Advanced RAG Agent Agentic AI Agents AI-as-a-Judge AI Engineering AI Judges AI Orchestration Alignment Alignment Applications of Foundation Models Automatic1111 Autoregressive language models

b

Benchmarks BM25

c

Causal language models Chain-of-thought (CoT)Chain of Thought Prompting Chains ChatGPT Chroma Chunking Chunking Module Chunking Strategies Civitai Claude Cleaning Layer Cohere Constraint Sampling Context Context WIndow Continuous batching Copyright Regurgitation Copyright Regurgitation Cost and Latency Cross entropy

d

Data Category Data Collection Pipeline Data Contamination Data Extraction Module Data Manipulation in Foundation Models Data parallelism Data Quality Concerns Data Slicing Data Synthesis Data Theft Dataset Engineering Deterministic Dispatcher Domain-Specific Models Domain-specific models

e

Eleven Labs Embedding-based retrieval Embedding Component Embedding Handler Embeddings Evaluation Harness Evaluation Pipeline ExLlamaV2 EXL2

f

Factual Consistency Faiss Feature Feature Pipeline Few-shot learning Few-shot learning Finetuning Finetuning Finetuning for Structured Outputs Flash attention Foundation Models Frontier Model Function calling

g

Gemini Generative AI GGUF GGUF Format Google AI Studio GPTQ Gradient Descent Greedy sampling Greedy Sampling Grounding Gwen

h

Hallucination Handler Hugging Face Hybrid Search Hypothetical Document Embeddings

i

In-context learning Inference Inference Optimization Instruction Following Instruction-Following Capability Inverse Document Frequency Inverted index

j

JSON mode

k

k-nearest neighbors Kaggle KV cache

l

Large Language Model Latent Space Lexical Similarity Llama LLM Engineer LLM Twin LLM Twin Architecture LLM Twin Goal Loading Module Logical Feature Store Logits Logits Logprobs Luma Labs

m

Masked language models Memory System Methods for Structured Outputs Midjourney Mistral Mixture-of-experts (MoE)ML Engineering / MLOps Model Adaptation Model API Cons Model API Pros Model Build vs. Buy Model Context Protocol Model Development Process Model Licenses Model Optimization Strategies Model parallelism Model Quantization Multilingual Models Multimodal Embedding Models

n

N-grams Natural Language Generation (NLG)NoSQL Database Notebook LM

o

Ollama One Shot Prompting Open-ended Outputs Open Source Models Open Weight Models OVM

p

PagedAttention Parameter Perplexity Perplexity Perplexity AI Pinecone Pipeline parallelism Post-Processing Post-retrieval Post-training Post-Training Quantization (PTQ)Pre-retrieval Pre-retrieval Pre-trained model Preference Models Probabilistic Prompt Engineering Prompt Engineering Prompt Engineering Prompt Injection Prompt Organization Prompt Security Prompting Guide Proprietary Data

q

Quantization Quantization-Aware Training

r

RAG RAG Applications RAG Ingestion Pipeline Redundancy Removal Reinforcement Learning from Human Feedback Reinforcement Learning from Human Feedback Reranking Retrieval Algorithms Retrieval-Augmented Generation Retrieval Augmented Generation Retrieval Pipeline Reverse Prompt Engineering Reward Model Roleplaying RunDiffusion Runway ML

s

SAFE Sampling Self-critique prompting Self-Hosting Cons Self-Hosting Pros Self-query Self-supervision Semantic Similarity Speculative decoding Stability.ai Stable Diffusion Stable Diffusion Art Stochastic Parrot Structured Outputs Structured Outputs Sub-queries Supervised Finetuning System Prompt

t

Temperature Tensor Parallelism Term-based retrieval Term Frequency Term Frequency-Inverse Document Frequency Test Time Sampling Text Generation Inference Text-to-SQL TGI Time to first token Token Token Tokenization Tokenization Top-k sampling Top-p sampling Toxicity Benchmarks Training Data Extraction Training Data Sources

u

Udio

v

vLLM Vector Database Vector Database Vector database Vocabulary

w

Weight quantization

z

ZenML Pipeline zenml.io Zero Shot Prompting