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Prompt Engineering

Prompt design, context engineering, and optimization

6 entities in this channel

ToolPrompt Engineering

PromptLayer

by PromptLayer

PromptLayer is a prompt management and observability platform that acts as a middleware layer between application code and LLM APIs. It logs all prompt requests, supports visual prompt editing and versioning, and provides analytics on latency, cost, and quality across prompt versions.

prompt-engineeringobservabilitylogging
44C
ToolPrompt Engineering

Mirascope

by Mirascope

Mirascope is a Pythonic library for building LLM applications with type-safe prompt engineering. It uses decorators and dataclasses to define prompts as code, supports provider-agnostic function calling, and integrates with Lilypad for prompt versioning and tracing.

prompt-engineeringpythontype-safe
44C
ToolPrompt Engineering

Maxim AI

by Maxim AI

Maxim AI is a prompt engineering and evaluation platform with a visual prompt studio, automated test generation, and multi-model comparison capabilities. It focuses on making prompt iteration fast and data-driven with built-in regression testing and output scoring.

prompt-engineeringvisual-studioevaluation
44C
ToolPrompt Engineering

LangSmith Prompt Hub

by LangChain

LangSmith Prompt Hub is a collaborative prompt management platform integrated into LangSmith. Teams can version, share, and iterate on prompts with A/B testing, performance tracking, and direct integration into LangChain and LangGraph applications.

prompt-engineeringversioningcollaboration
44C
ToolPrompt Engineering

Humanloop

by Humanloop

Humanloop is a prompt management and evaluation platform designed for enterprise LLM teams. It provides prompt versioning, collaborative editing, automated evaluation pipelines, human feedback collection, and model comparison to systematically improve prompt quality in production.

prompt-engineeringenterpriseversioning
44C
ToolPrompt Engineering

DSPy

by Stanford NLP

DSPy compiles natural-language programs into optimized prompt instructions using automatic few-shot example selection, instruction synthesis, and model finetuning. It eliminates brittle manual prompt engineering by treating prompts as learnable parameters in an optimization loop.

prompt-engineeringoptimizationcompilation
44C