How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge

Your LLM app is more than a single API call. Build a full AI stack to manage hallucinations, stale data, and high operational costs.

#1about 2 minutes

The exciting and overwhelming pace of AI development

The rapid evolution of AI creates both excitement for new possibilities and anxiety about keeping up with new models and papers.

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Choosing the right AI-powered developer tools and IDEs

Developers are using a mix of IDEs like VS Code and browser-based environments like IDX, enhanced with AI assistants like Gemini Code Assist.

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Understanding the fundamental concepts behind LLMs

Exploring foundational LLM questions, such as why they use tokens or struggle with math, is key to understanding their capabilities and limitations.

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Why LLMs require pre- and post-processing pipelines

Real-world LLM applications are more than a single API call, requiring data pre-processing and output post-processing for reliable results.

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Balancing creativity and structure in LLM outputs

Using a multi-step process, where an initial creative generation is followed by structured extraction, can yield better and more reliable results.

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Mitigating LLM hallucinations with data grounding

Grounding LLM responses with external data from sources like Google Search or a private RAG pipeline is essential for preventing hallucinations.

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Overcoming the challenge of stale data in LLMs

Use techniques like RAG with up-to-date private data or provide the LLM with tools to call external APIs for live information.

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Managing the cost of long context windows

Reduce the cost and latency of large inputs by using techniques like context caching for reusable data and batch generation for parallel processing.

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Ensuring data quality and security in LLM systems

Implement guardrails, PII redaction, and proper data filtering to prevent garbage outputs and protect sensitive information in your LLM applications.

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Exploring the rise of agentic AI systems

Agentic AI involves systems that can act on a user's behalf, but their development requires a strong focus on security and sandboxed environments to be safe.

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The future of LLMs as a seamless user experience

The ultimate success of generative AI will be its seamless and invisible integration into everyday applications, improving the user experience without requiring separate apps.

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Avoiding the chatbot trap with a human handoff

A critical mistake in AI implementation is failing to provide a clear and accessible path for users to connect with a human when the AI cannot resolve their issue.

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How to stay current in the fast-paced field of AI

To keep up with AI developments, follow curated newsletters and credible sources to understand emerging trends and discover new possibilities for your applications.