What are some open-source options for prompt eval?
Prompt evaluation is a critical step in developing effective LLM applications. It ensures that prompts produce accurate, relevant, and unbiased outputs, improving user experience and reducing the risk of ‘hallucination’ or misinformation. As LLMs become increasingly integrated into workflows, reliable prompt evaluation becomes essential for maintaining high-quality performance and reducing operational costs.
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This article briefly encapsulates many of the flaws with viewing responses from GPTs as truth. Many models 'hallucinate' and even present misinformation, sometimes with dubious consequences. We read in another article of a situation in which a lawyer cited several cases he found on ChatGPT it was discovered that the cases he used were completely fabricated by the LLM.
Scenarios such as these present the importance of Prompt evaluation. Improving accuracy through rigorous quality assurance routines help maintain the validity of a model's responses, thereby increasing the production of high-quality and relevant outputs.
On top of quality assurance, there must be a focus on the performance and efficiency of an LLM. The identification and refining of accurate prompts increase throughput and decrease response times from the model, lowering periods of undesired latency. This encourages the efficient use of resources that could significantly lower costs for the providers of the LLM.
What also needs to be considered is the potential for bias. When a user of a GPT or LLM queries information, it is imperative that what is returned is presented with neutrality and not enforcing potentially offensive stereotypes or misinformation. Having a thorough evaluation process will ultimately enhance the overall user experience, leading to better sessions with the model.
Open Source Tools for Prompt Evaluation
There are several open-source tools crafted to facilitate LLM output.
OpenAI Eval Toolkit: Provides developers with a toolkit to create custom prompt evaluations. It offers quick testing and feedback loops to refine model outputs.
import openai
import openai_eval
# Define a custom evaluation
def custom_eval(prompt):
model = "gpt-3.5-turbo"
response = openai_eval.run_eval(model, prompt)
return response
# Example prompt
prompt = "What is the capital of France?"
result = custom_eval(prompt)
print(f"Model response: {result}")
LangChain:A powerful tool for chaining prompts and improving workflows. It allows developers to evaluate and adjust LLM outputs across various stages.
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import OpenAI
# Define the prompt template
template = "What are the top 3 use cases for {technology}?"
prompt = PromptTemplate(input_variables=["technology"], template=template)
# Chain the LLM to process the prompt
llm = OpenAI(model_name="text-davinci-003")
llm_chain = LLMChain(prompt=prompt, llm=llm)
# Run the evaluation
response = llm_chain.run(technology="AI in healthcare")
print(response)Hugging Face’s Evaluate Library: Seamlessly integrates with the transformers library, automating and standardizing prompt evaluations. It provides a versatile approach for developers to analyze and improve model outputs.
from transformers import pipeline
from evaluate import evaluator
# Load the model
classifier = pipeline("sentiment-analysis")
# Define the evaluation
eval = evaluator("text-classification")
results = eval.compute(
model_or_pipeline=classifier,
data=[{"text": "I love AI tools!"}],
)
print(f"Evaluation results: {results}")The Future of Prompt Evaluation & Integrating it into Your Workflow
Artificial Intelligence isn't going anywhere. These systems are going to continue to progress and evolve and as they do, prompt evaluation is only going to play a more pivotal role in the evolution of LLMs. Users can expect advances in response validity and reduction of bias, thereby causing daily adoption to continue to increase.
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This blog is written by Jeremy Rivera at KushoAI. We're building an AI agent that tests your APIs for you. Bring in API information and watch KushoAI turn it into fully functional and exhaustive test suites in minutes.
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