EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Jul 22·5 min read
Six questions before you add an LLM
TL;DR
An AI consultant attempted to automate lead discovery using a large language model (LLM) but faced significant issues with accuracy and debugging. The experience highlights the need for a clear problem definition before implementing AI solutions.
✦ Why It Matters
Before adopting AI, clearly define the problem you aim to solve to ensure effective implementation.
Key Takeaways
- Identify the specific problem before considering LLMs.
- Assess if your workflow can be fully specified in advance.
- Verify outputs cheaply and accurately to ensure reliability.
- Combine LLMs with deterministic code for optimal results.
How It Works
The consultant's prospect discovery system initially relied entirely on an LLM to handle web searches, deduplication, validation, and database insertion. However, the complexity of the workflow led to inconsistent outputs and difficulties in debugging.
By breaking down the process and evaluating each step, the consultant identified where deterministic code could effectively replace the LLM, improving accuracy and efficiency.