Confident AI
What is Confident AI?
Confident AI is an all-in-one LLM evaluation platform designed to help companies justify the production readiness of their Language Model applications. It offers 14+ metrics, dataset management, monitoring, human feedback integration, and works with the DeepEval open framework.
How to use Confident AI?
Using Confident AI is straightforward. Conduct LLM experiments, manage datasets, and monitor performance. Integrate human feedback for automatic improvements in LLM applications. Start by setting up experiments and evaluating the results.
Confident AI's Core Features
14+ metrics for LLM experiments
Dataset management
Performance monitoring
Human feedback integration
Works with DeepEval framework
Confident AI's Use Cases
Companies can use Confident AI to assess the readiness of their LLM applications for production deployment.
FAQ from Confident AI
Can Confident AI be used for any size of company?
How can Confident AI help improve LLM apps automatically?
Confident AI Company
Confident AI Company name: Confident AI Inc. .
Confident AI Pricing
Confident AI Pricing Link: https://www.confident-ai.com/pricing
Confident AI Github
Confident AI Github Link: https://github.com/confident-ai/deepeval
Confident AI Reviews (0)
Analytic of Confident AI
Confident AI Website Traffic Analysis
Geography
Top 5 Regions
Confident AI Launch embeds
Use website badges to drive support from your community for your Toolify Launch. They're easy to embed on your homepage or footer.
Social Listening
How to Setup DeepEval for Fast, Easy, and Powerful LLM Evaluations
Quickly get started running evals for your LLMs with Open-Source framework DeepEval. This is a quick how-to tutorial on how-to get started using the DeepEval framework. Evaluating LLMs is critical for improving performance and guaranteeing reliability for production LLM applications. At Eigen, we run evals on all our applications to ensure we’re meeting required thresholds and find areas where we can improve. If you’re looking to build production AI applications contact us at eigen.net. Follow along the Quick Introduction in the DeepEval documentation. https://docs.confident-ai.com/docs/getting-started Make sure to create a free account to view your eval results at http://confident-ai.com and run the command “deepeval login” in your terminal to automatically view your results in the web app. Here are the commands to run if you're following along: **Setup Python Virtual Environment** python3 -m venv venv source venv/bin/activate **Install DeepEval** pip install -U deepeval **Set OpenAI API Key as Env. Variable** export OPENAI_API_KEY=yourAPIkey **Create file to run test** touch test_example.py **Paste code below to test_example.py** ____________________ from deepeval import assert_test from deepeval.test_case import LLMTestCase from deepeval.metrics import AnswerRelevancyMetric def test_answer_relevancy(): answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.5) test_case = LLMTestCase( input="What if these shoes don't fit?", actual_output="We offer a 30-day full refund at no extra cost." ) assert_test(test_case, [answer_relevancy_metric]) **Command For Testing First Eval** deepeval test run test_example.py
How to fine-tune an LLM? Getting started
In the first episode of the AI, LLM, and GraphRAG series, Code with Buda takes you on a hands-on journey about how to fine tune an LLM. The topics covered are: 1. How to Generate a Training Dataset 2. How to Fine-tune an LLM to Learn Cypher 3. How to Brainstorm Model Validation Techniques You can check out Marko on Github here: https://github.com/gitbuda If you are interested in the topic, learn more about GraphRAG and how companies are using it with Memgraph: 1. Memgraph Academy - Stay one node ahead: https://memgraph.com/academy/enhancing-ai-with-graph-databases-and-llms 2. Microchip Optimises LLM Chatbot with RAG and a Knowledge Graph: https://memgraph.com/webinars/microchip-optimizes-llm-chatbot-with-rag-and-a-knowledge-graph 3. Optimising Insulin Management: The Role of GraphRAG in Patient Care: https://memgraph.com/webinars/optimizing-insulin-management-the-role-of-graphrag-in-patient-care --- About Memgraph: Memgraph offers a light and powerful graph platform comprising the Memgraph Graph Database, MAGE Library, and Memgraph Lab Visualization. Memgraph is a dynamic, lightweight graph database optimized for analyzing data, relationships, and dependencies quickly and efficiently. It comes with a rich suite of pre-built deep path traversal algorithms and a library of traditional, dynamic, and ML algorithms tailored for advanced graph analysis, making Memgraph an excellent choice in critical decision-making scenarios such as risk assessment (fraud detection, cybersecurity threat analysis, & criminal risk assessment), 360-degree data and network exploration [Identity and Access Management (IAM), Master Data Management (MDM), & Bill of Materials (BOM)], and logistics and network optimization. Website: https://www.memgraph.com Twitter: https://www.twitter.com/memgraphdb LinkedIn: https://www.linkedin.com/company/memgraph Facebook: https://www.facebook.com/memgraph --- 00:00:00 Intro 00:02:51 Star explaining setup and the work so far 00:04:27 Quick unsloth.ai explanation 00:05:09 Continue with the setup and the context 00:26:14 Centralize templates 01:18:49 Play with the base model 02:24:20 Refactor and play with the models 02:46:26 Deepevel setup (some unrelated issues) 03:11:57 Playing with deepevel (docs.confident-ai.com) 03:18:52 Setup the OpenAI 03:28:43 Pause (skip this completely) 03:37:23 Getting deepeval work with local GPU running ollama model (llama3)