Blog: LLM Stack - added keywords for SEO #2612
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LinaLam
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Sep 12, 2024
LinaLam
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The latest updates on your projects. Learn more about Vercel for Git ↗︎
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| ## Why a New Stack for LLM Applications? | ||
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| LLM applications are deceptively simple to kickstart, but scaling them unveils a Pandora's box of challenges: | ||
| LLM applications, powered by pre-trained models, are deceptively simple to kickstart, but scaling them unveils unique challenges in machine learning and data processing: |
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| LLM applications, powered by pre-trained models, are deceptively simple to kickstart, but scaling them unveils unique challenges in machine learning and data processing: | |
| LLM applications, powered by pre-trained models, are deceptively simple to kickstart, but scaling them unveils unique challenges: |
"machine learning and data processing" doesn't make too much sense.
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| - Increased focus on production-ready LLM applications | ||
| - Growing emphasis on observability and data versioning | ||
| - Introduction of enterprise features to foundational components |
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What does this mean?
| LLM applications are deceptively simple to kickstart, but scaling them unveils a Pandora's box of challenges: | ||
| LLM applications, powered by pre-trained models, are deceptively simple to kickstart, but scaling them unveils unique challenges in machine learning and data processing: | ||
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| 1. **Platform Limitations**: Traditional stacks struggle with LLM app demands, especially when dealing with real-time data pipelines. |
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| 1. **Platform Limitations**: Traditional stacks struggle with LLM app demands, especially when dealing with real-time data pipelines. | |
| 1. **Platform Limitations**: Traditional stacks struggle with the unique demands of LLM apps. |
Doesn't make much sense as is
| LLM applications, powered by pre-trained models, are deceptively simple to kickstart, but scaling them unveils unique challenges in machine learning and data processing: | ||
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| 1. **Platform Limitations**: Traditional stacks struggle with LLM app demands, especially when dealing with real-time data pipelines. | ||
| 2. **Tooling Gaps**: Existing tools often fall short in managing LLM-specific workflows, including **<span style={{color: '#0ea5e9'}}>prompt engineering</span>** and efficient API calls. |
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| 2. **Tooling Gaps**: Existing tools often fall short in managing LLM-specific workflows, including **<span style={{color: '#0ea5e9'}}>prompt engineering</span>** and efficient API calls. | |
| 2. **Tooling Gaps**: Existing tools often fall short in managing LLM-specific workflows, including **<span style={{color: '#0ea5e9'}}>prompt engineering</span>** and evaluations. |
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| 1. **Platform Limitations**: Traditional stacks struggle with LLM app demands, especially when dealing with real-time data pipelines. | ||
| 2. **Tooling Gaps**: Existing tools often fall short in managing LLM-specific workflows, including **<span style={{color: '#0ea5e9'}}>prompt engineering</span>** and efficient API calls. | ||
| 3. **Observability Hurdles**: Monitoring LLM performance requires specialized solutions for tracking **<span style={{color: '#0ea5e9'}}>embedding models</span>** and external API usage. |
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| 3. **Observability Hurdles**: Monitoring LLM performance requires specialized solutions for tracking **<span style={{color: '#0ea5e9'}}>embedding models</span>** and external API usage. | |
| 3. **Observability Hurdles**: Monitoring LLM performance requires specialized solutions for tracking **<span style={{color: '#0ea5e9'}}>agentic workflows</span>**, multi-modal models, and more |
| 1. **Platform Limitations**: Traditional stacks struggle with LLM app demands, especially when dealing with real-time data pipelines. | ||
| 2. **Tooling Gaps**: Existing tools often fall short in managing LLM-specific workflows, including **<span style={{color: '#0ea5e9'}}>prompt engineering</span>** and efficient API calls. | ||
| 3. **Observability Hurdles**: Monitoring LLM performance requires specialized solutions for tracking **<span style={{color: '#0ea5e9'}}>embedding models</span>** and external API usage. | ||
| 4. **Security Concerns**: LLMs introduce new vectors for data breaches and **<span style={{color: '#0ea5e9'}}>prompt injections</span>**, necessitating robust data processing protocols. |
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| 4. **Security Concerns**: LLMs introduce new vectors for data breaches and **<span style={{color: '#0ea5e9'}}>prompt injections</span>**, necessitating robust data processing protocols. | |
| 4. **Security Concerns**: LLMs introduce new vectors for data breaches and **<span style={{color: '#0ea5e9'}}>prompt injections</span>**. |
honestly doesn't make much sense
| Helicone isn't just another tool in the LLM stack - it's a game-changer for building AI applications. Our primary focus areas, Gateway and Observability, are crucial for building robust, scalable LLM applications. We offer: | ||
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| 1. **Efficient LLM Cache**: Optimize your API calls and reduce costs. | ||
| 2. **Real-Time Monitoring**: Track your embedding models and external API usage. |
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| 2. **Real-Time Monitoring**: Track your embedding models and external API usage. | |
| 2. **Real-Time Monitoring**: Track your LLM requests & responses + metadata |
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| 1. **Efficient LLM Cache**: Optimize your API calls and reduce costs. | ||
| 2. **Real-Time Monitoring**: Track your embedding models and external API usage. | ||
| 3. **Orchestration Framework**: Streamline your data processing and machine learning workflows. |
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| 3. **Orchestration Framework**: Streamline your data processing and machine learning workflows. | |
| 3. **Orchestration Framework**: Streamline your data processing. |
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| Ready to supercharge your LLM development? Dive deeper into Helicone's capabilities and see how it can transform your LLM stack today! | ||
| The LLM Stack is revolutionizing AI application development, enabling more robust and scalable solutions. As LLM architectures evolve, the focus is on creating cost-effective, powerful AI systems that expand the frontiers of machine learning and artificial intelligence. This new paradigm empowers developers to meet the growing demands of our AI-driven world with increased efficiency and innovation. |
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I'd just remove "The Future of LLM Applications" section because doesn't really have any good substance
| Before you continue, please first read the companion blog on [The Evolution of LLM Architecture](/blog/building-an-llm-stack). | ||
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| ## The Evolution of LLM Applications |
LinaLam
changed the title
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