2,263 technical terms and definitions
prompt engineering context optimization, prompt engineering, engineering context, cot llm rag, prompt engineering context, engineering context optimization, context optimization
Prompt Engineering and Context Optimization Introduction & Motivation Effective prompts dramatically improve LLM outputs for engineering tasks. Prompt engineering combines domain…
prompting
Prompt engineering for RAG is the design of instructions, context formatting, and response constraints that guide the model to use retrieved evidence correctly - prompt quality…
Prompt Engineering for Multimodal Models Introduction & Motivation Prompt Engineering for Multimodal Models addresses a central problem in multimodal learning…
prompt engineering optimization, prompt engineering, engineering optimization
Prompt Engineering Optimization Introduction & Motivation Prompt Engineering Optimization addresses a central problem in nlp process control…
prompt engineering, engineering techniques, glue llm
Prompt Engineering Techniques Introduction & Motivation Prompt engineering: craft inputs for optimal LLM output. Critical for zero-shot and few-shot learning. Applications…
prompt engineering
Prompt ensemble is the technique of combining predictions from multiple different prompts for the same task — leveraging the diversity of prompt formulations to produce more…
jailbreak, llm security, adversarial prompts, red teaming, guardrails, safety bypass, input sanitization
Prompt injection exploits a design fact about how large language models work: instructions and data arrive through the exact same channel, as tokens in the same context window…
ai safety
Prompt injection exploits a design fact about how large language models work: instructions and data arrive through the exact same channel, as tokens in the same context window…
ai safety
Prompt Injection is an attack technique that embeds malicious instructions in untrusted input to override intended model behavior - It is a core method in modern AI safety…
ai safety
Prompt injection attacks is the adversarial technique where untrusted input contains instructions intended to override or subvert system-defined model behavior - it is a primary…
system
Prompt Injection Defense What is Prompt Injection? Attacks where user input manipulates LLM behavior, bypassing intended instructions. Attack Types Attack Example Direct injection…
ai safety
Prompt injection defense is the set of architectural and prompt-level controls designed to prevent untrusted text from overriding trusted instructions or triggering unsafe actions…
ai safety
Prompt Leaking is the attack technique that extracts hidden system prompts, instructions, and confidential configurations from AI applications — enabling adversaries to reveal…
prompting techniques
Prompt Mining is the extraction of high-performing prompt patterns from existing corpora, logs, or historical experiments - It is a core method in modern LLM execution workflows.…
ai safety
Prompt moderation is the pre-inference safety process that evaluates user prompts for harmful intent, policy violations, or attack patterns before model execution - it reduces…
prompt engineering
Prompt Optimization is the systematic, automated process of improving prompts through search, gradient-based methods, or LLM-guided rewriting rather than manual trial-and-error…
prompting techniques
Prompt Optimization is the systematic search for prompt formulations that maximize task performance under defined metrics - It is a core method in modern LLM execution workflows.…
prompt engineering, templates, few-shot, chain of thought, role prompting
Prompt engineering patterns are reusable templates and techniques for structuring LLM interactions — providing proven approaches like few-shot examples, chain-of-thought…
prompting techniques
Prompt Sensitivity is the degree to which model outputs change in response to small prompt wording or formatting variations - It is a core method in modern LLM execution…
prompting
Prompt syntax is the formal text structure and token conventions used by a generation system to interpret user instructions - it determines how phrases, separators, weights…
prompting techniques
Prompt Template is a reusable prompt artifact with placeholders and fixed instruction blocks for repeatable model interactions - It is a core method in modern LLM workflow…
prompting
Prompt templates is the reusable prompt structure with parameterized fields that standardizes model inputs across repeated tasks - templates improve consistency, maintainability…
multimodal ai
Prompt-to-Prompt is a diffusion editing technique that modifies generated content by changing prompt text while preserving layout - It allows semantic edits without rebuilding…
generative models
Prompt-to-Prompt Editing is a text-guided image editing technique for diffusion models that modifies generated images by manipulating the cross-attention maps between text tokens…
generative models
Prompt truncation is the automatic removal of tokens beyond encoder context length when prompt input exceeds model limits - it is a common but often hidden behavior that can…
fine-tuning
Prompt tuning learns continuous "soft prompts" while keeping the base model frozen. Mechanism: Prepend learned embedding vectors to input, these vectors trained via…
prompting techniques
Prompt Tuning is a parameter-efficient adaptation method that learns virtual prompt embeddings while keeping base model weights frozen - It is a core method in modern LLM…
prefix tuning, soft prompt, learnable prompt, p tuning, prompt based fine tuning
Prompt Tuning and Prefix Tuning are the parameter-efficient fine-tuning methods that prepend small sequences of learnable "soft" token embeddings to the input or intermediate…
Soft Prompt Tuning for Extreme Parameter Efficiency Introduction & Motivation Soft Prompt Tuning for Extreme Parameter Efficiency addresses a central problem in transfer learning…
version control
Prompt Versioning and Management Why Version Prompts? Prompts are code. Track changes, roll back issues, and collaborate effectively. Git-Based Versioning Metadata Format Prompt…
prompt engineering
Prompt weighting assigns different importance levels to different parts of a text prompt. Syntax examples: AUTOMATIC1111 uses (word:weight), Midjourney uses ::weight suffix…
generative models
Prompt weighting is the method of assigning relative importance to prompt tokens or phrase groups to prioritize selected concepts - it helps resolve conflicts when multiple…
computer vision
Promptable Segmentation is a paradigm where segmentation masks are generated based on user inputs — allowing users to interactively define what to cut out using points, bounding…
testing, eval
Promptfoo is an open-source command-line tool for systematically testing and evaluating LLM prompts across multiple models and providers — enabling developers to define test cases…
logging, versioning
PromptLayer is a platform for logging, versioning, A/B testing, and evaluating LLM prompts — sitting as a transparent middleware layer between your application and LLM providers…
nlp
Pronoun resolution is the task of identifying what a pronoun refers to in a sentence or paragraph. In natural language processing, it is a core part of coreference resolution…
reasoning
Proof generation involves creating rigorous mathematical proofs that demonstrate the truth of mathematical statements through logical deduction from axioms and previously proven…
proof of stake consensus, proof stake consensus, proof stake, stake consensus
Proof of Stake Consensus Introduction & Motivation Proof of Stake Consensus addresses a central problem in blockchain process control and stake-requirement-penalty prediction: how…
proof of work mining, proof work mining, proof work, work mining
Proof of Work Mining Introduction & Motivation Proof of Work Mining addresses a central problem in blockchain process control and difficulty-adjustment-mechanism prediction: how…
Propensity Score Matching Introduction & Motivation Propensity Score Matching addresses a central problem in estimating causal effects with causalml methods: how to Estimate…
Propensity Score Matching Methods Introduction & Motivation Propensity Score Matching Methods addresses a central problem in causal inference and {target} analysis: how to Apply…
propensity score matching observational prediction, propensity score matching, score matching observational, matching observational prediction, propensity score
Propensity Score Matching Observational Prediction with Machine Learning Introduction & Motivation Propensity Score Matching Observational Prediction with Machine Learning…
Propensity Score Matching and Nearest-Neighbor Caliper Introduction & Motivation Propensity Score Matching and Nearest-Neighbor Caliper addresses a central problem in causal…
Propensity Score Matching (Extended Study) Introduction & Motivation Propensity Score Matching (Extended Study) addresses a central problem in estimating causal effects…
Propensity Score Matching (Extended Study) Introduction & Motivation Propensity Score Matching (Extended Study) addresses a central problem in estimating causal effects…
Propensity Score Matching (Extended Study) Introduction & Motivation Propensity Score Matching (Extended Study) addresses a central problem in estimating causal effects…
recommendation systems
Propensity Score Rec is causal recommendation using propensity estimates to balance treated and untreated exposure groups. - It approximates randomized comparison…
Continuous Ranked Probability Score CRPS Evaluation Introduction & Motivation Continuous Ranked Probability Score CRPS Evaluation addresses a central problem in uncertainty…
code ai
Property-Based Test Generation is the AI task of identifying and generating invariants, algebraic laws, and universal properties that a function must satisfy for all valid inputs…
property-based testing frameworks, property-based testing, testing frameworks
Property-Based Testing Frameworks Introduction & Motivation Property-Based Testing Frameworks addresses a central problem in verification process control…