AI-Driven Search Accuracy Enhancement
Budget: $10 – $30 AUD
I’m building a new search experience and my top priority is accuracy. I need an AI expert who can design, train, and integrate models that consistently surface the most relevant results. The focus is strictly on boosting search accuracy; personalisation and analytics can come later once the core engine is rock-solid.
The data you’ll be working with is broad: “AI based across all data-based AI platforms.” In practice that means you may pull from large-scale web-crawled corpora, user-interaction logs, or specialised databases—whatever combination proves most effective. I’m open to your recommendations on optimal pipelines, vector stores, or fine-tuning strategies, provided they align with scalable, production-ready architecture.
Key deliverables
• A trained model (or ensemble) that demonstrably improves precision and recall over a strong baseline
• Integration code or API endpoints ready to slot into my existing search stack
• A concise evaluation report with reproducible metrics and test scripts
Acceptance criteria
• At least a 15 % uplift in top-10 precision on our validation set
• Latency below 150 ms for an average query on commodity cloud hardware
• Clear documentation covering data handling, training regimen, and deployment steps
Please outline your relevant experience with search ranking models, LLM re-ranking, embeddings, or similar technologies and share a brief plan for achieving the above targets.
The data you’ll be working with is broad: “AI based across all data-based AI platforms.” In practice that means you may pull from large-scale web-crawled corpora, user-interaction logs, or specialised databases—whatever combination proves most effective. I’m open to your recommendations on optimal pipelines, vector stores, or fine-tuning strategies, provided they align with scalable, production-ready architecture.
Key deliverables
• A trained model (or ensemble) that demonstrably improves precision and recall over a strong baseline
• Integration code or API endpoints ready to slot into my existing search stack
• A concise evaluation report with reproducible metrics and test scripts
Acceptance criteria
• At least a 15 % uplift in top-10 precision on our validation set
• Latency below 150 ms for an average query on commodity cloud hardware
• Clear documentation covering data handling, training regimen, and deployment steps
Please outline your relevant experience with search ranking models, LLM re-ranking, embeddings, or similar technologies and share a brief plan for achieving the above targets.