Ship AI to prod 10x faster
Ship AI to prod 10x faster
Aligning your AI models with customer needs
By integrating customer insights into the design and training of AI systems, businesses can create more relevant, personalized, and impactful experiences.
Integrated with
Faster Model Improvement
1 week vs 12 weeks of effort in iteration of model for better outputs
1 week vs 12 weeks of effort in iteration of model for better outputs
Faster Prompt Optimization
1 hour vs 10 hours of trial and errors by prompt engineers
1 hour vs 10 hours of trial and errors by prompt engineers
Time of AI saved
Data availability, cleaning, preparation solved, QA solved, Optimisation solved
Data availability, cleaning, preparation solved, QA solved, Optimisation solved
Time of AI saved
Data availability, cleaning, preparation solved, QA solved, Optimisation solved
Are you drowning in data while your AI stagnates?
Empower AI teams with our end-to-end Data Layer, automating everything from training to testing, observability to iterations. Imagine slashing 80% of your data management workload, freeing your team to focus on innovation. With Future AGI, you'll deliver high-quality AI products faster, supercharge your ROI, and leave the competition in the dust.
Introducing Future AGI
Unlock the full potential of your AI with AIForge:
Unlock the full potential of your AI with AIForge:
Unlock the full potential of your AI with AIForge:
Unlock the full potential of your AI with AIForge:
Our Comprehensive
Our Comprehensive
Our Comprehensive
Our Comprehensive
AI Development Ecosystem
AI Development Ecosystem
AI Development Ecosystem
AI Development Ecosystem
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Build & Experiment
Optimize
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Build & Experiment
Optimize
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Build & Experiment
Optimize
Observe
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Evaluate
Build & Experiment
Optimize
Observe
Annotate
Articles to help you
Perfecting AI Models With Future AGI’s Experiment Feature
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Benchmarking LLMs for Business Applications
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Optimizing Non-Deterministic LLM Prompts with Future AGI
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Perfecting AI Models With Future AGI’s Experiment Feature
This article explores the framework of Retrieval-Augmented Generation (RAG), emphasizing the importance of creating high-precision document embeddings for improved contextual retrieval. It introduces chunking, a method of splitting documents into semantically coherent parts, and examines various chunking strategies, such as fixed-size, delimiter-based, sentence-level, semantic, and agentic chunking. The article evaluates these methods using metrics like LDA coherence scores and Intersection over Union (IoU), highlighting semantic and agentic chunking as the most effective but resource-intensive approaches. Finally, it provides practical applications and trade-offs of each method for creating meaningful text segments.
Read More
Benchmarking LLMs for Business Applications
This article explores the framework of Retrieval-Augmented Generation (RAG), emphasizing the importance of creating high-precision document embeddings for improved contextual retrieval. It introduces chunking, a method of splitting documents into semantically coherent parts, and examines various chunking strategies, such as fixed-size, delimiter-based, sentence-level, semantic, and agentic chunking. The article evaluates these methods using metrics like LDA coherence scores and Intersection over Union (IoU), highlighting semantic and agentic chunking as the most effective but resource-intensive approaches. Finally, it provides practical applications and trade-offs of each method for creating meaningful text segments.
Read More
Perfecting AI Models With Future AGI’s Experiment Feature
This article explores the framework of Retrieval-Augmented Generation (RAG), emphasizing the importance of creating high-precision document embeddings for improved contextual retrieval. It introduces chunking, a method of splitting documents into semantically coherent parts, and examines various chunking strategies, such as fixed-size, delimiter-based, sentence-level, semantic, and agentic chunking. The article evaluates these methods using metrics like LDA coherence scores and Intersection over Union (IoU), highlighting semantic and agentic chunking as the most effective but resource-intensive approaches. Finally, it provides practical applications and trade-offs of each method for creating meaningful text segments.
Read More
Benchmarking LLMs for Business Applications
This article explores the framework of Retrieval-Augmented Generation (RAG), emphasizing the importance of creating high-precision document embeddings for improved contextual retrieval. It introduces chunking, a method of splitting documents into semantically coherent parts, and examines various chunking strategies, such as fixed-size, delimiter-based, sentence-level, semantic, and agentic chunking. The article evaluates these methods using metrics like LDA coherence scores and Intersection over Union (IoU), highlighting semantic and agentic chunking as the most effective but resource-intensive approaches. Finally, it provides practical applications and trade-offs of each method for creating meaningful text segments.
Read More
Perfecting AI Models With Future AGI’s Experiment Feature
This article explores the framework of Retrieval-Augmented Generation (RAG), emphasizing the importance of creating high-precision document embeddings for improved contextual retrieval. It introduces chunking, a method of splitting documents into semantically coherent parts, and examines various chunking strategies, such as fixed-size, delimiter-based, sentence-level, semantic, and agentic chunking. The article evaluates these methods using metrics like LDA coherence scores and Intersection over Union (IoU), highlighting semantic and agentic chunking as the most effective but resource-intensive approaches. Finally, it provides practical applications and trade-offs of each method for creating meaningful text segments.
Read More
Benchmarking LLMs for Business Applications
This article explores the framework of Retrieval-Augmented Generation (RAG), emphasizing the importance of creating high-precision document embeddings for improved contextual retrieval. It introduces chunking, a method of splitting documents into semantically coherent parts, and examines various chunking strategies, such as fixed-size, delimiter-based, sentence-level, semantic, and agentic chunking. The article evaluates these methods using metrics like LDA coherence scores and Intersection over Union (IoU), highlighting semantic and agentic chunking as the most effective but resource-intensive approaches. Finally, it provides practical applications and trade-offs of each method for creating meaningful text segments.
Read More
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