Initially, the experience was positive, but it later became frustrating as the system started providing incorrect and repetitive answers. Upon reflection, I identified a few reasons for this issue: - The current retrieval system is too coarse, matching queries to the closest “question/topic” but struggling with variations, such as “symptoms of diabetes.” - The performance also depends on the dataset size; I uploaded relatively small files for my understanding. - TF-IDF sometimes selects the wrong match, like nutrition tips instead of FAQ answers. This leads to the perception that the agent is stuck providing irrelevant responses. To enhance robustness, consider the following strategies: - Expand the documentation. - Chunk large documents for better processing. - Improve the retrieval system. - Implement fallback rules to prevent nonsensical answers. To be continued… I’ll retrain my “nutrition‑obsessed” agent into something more balanced. Moral of the story: even AI agents need a diversified diet. 😅 learning starts and experience gains only when mistake happens.
Improving AI Agent Performance: Enhancing Retrieval and Fallbacks
This title was summarized by AI from the post below.
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Technology is advancing fast, but human expertise is still irreplaceable. A recent study published in Current Developments in Nutrition raised an important red flag about using artificial intelligence for dietary assessment. Researchers found that three popular large language models—ChatGPT, Claude, and Gemini—produced significant errors when estimating dietary intake from food photos. The biggest issues? Systematic underestimation of large portions and high variability in macronutrient calculations. The authors concluded that these general-purpose AI tools are not yet suitable for precise dietary assessment, especially in clinical or athletic settings where accuracy truly matters. I see more and more people turning to AI for nutrition advice—and while AI can be helpful for education and general guidance, nutrition is deeply personal. It involves context: health history, hormones, lifestyle, digestion, stress, goals, and patterns over time. Sometimes even a skilled professional needs careful assessment to connect all the dots. Expecting AI to replace that human judgment is unrealistic. AI can support health professionals—but it should never replace a qualified nutrition or health specialist.
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I love this🙌🏼 & hear it on the daily from my airmen… Constantly reminding them that “AI” is a great assistant when a nutritionist/CSCS cant be there but its not THE ANSWER… Just like Garmin/ Oura/Fitbit are amzing fitness tech but often dont tell the entire story - they help keep us aware, accountable & start the conversation but are NOT THE ANSWER that can replace what an actual trainer & SC can do…. Seen dangerous levels of reliance on tech to replace SMEs🤷🏼♀️😑👎🏼
Registered Dietitian | Sports & Performance Nutrition | Endurance & Hybrid Athletes | Corporate Wellness | Bilingual EN/ES
“ChatGPT said I should eat 2,100 calories.” The athlete training 10–12 hours per week asked me if that was “too much.” 🏃🏼♀️She’s running 40–50 km/week. 🏋🏼♀️Lifting 4x/week. 🏅 Trying to PR. 💻 Working a physically demanding job 45hr/week 2,100 wasn’t too much. It was barely maintenance. Here’s what AI doesn’t see: 👉🏻Your ferritin trending down over 18 months 👉🏻Subclinical hypothyroid markers creeping up 👉🏻A fasting glucose that’s rising despite high activity 👉🏻HRV suppression during peak training blocks 👉🏻A history of stress fractures 👉🏻Luteal phase disruption 👉🏻The fact that your “discipline” is actually low energy availability AI can calculate BMR. It cannot diagnose RED-S. It cannot see endocrine adaptation. It cannot see recovery debt. It cannot see the long-term cost of chronic underfueling. 🔢AI gives you numbers. Performance nutrition requires interpretation. Because in high-performing bodies, underfueling shows up as: 👉🏻Fatigue. 👉🏻Plateaus. 👉🏻Hormonal disruption. 👉🏻Injury. And by the time it’s obvious, you’re already behind. Information is cheap. Physiological context is not.
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