Finansal Araştırma için Yapay Zeka
Archivist’e bir soru sorun, dünyanın dört bir yanındaki borsa bildirimlerine dayalı yanıtlar alın.
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Sor
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Kaynak Referansları İçeren Yanıtlar Alın
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Doğrulayın
Herhangi bir alıntıya tıklayarak orijinal dosyayı açın. PDF görüntüleyici, ilgili bölümü tam olarak vurgular; böylece her iddiayı kaynak belgeyle karşılaştırarak doğrulayabilirsiniz.
Keşfedebileceğiniz daha pek çok şey var. İlk 10 sorgunuzu ücretsiz olarak kullanmaya başlayın.
Mimarinin Perde Arkası
Blogumuzda geliştirmekte olduğumuz şeylerden bahsediyoruz. Şeffaflık, ürünümüzün ta kendisidir.
A comprehensive overview of common AI misconceptions and the factual realities that should guide engineering decisions.
The math on autonomous agents is brutal. Chain 10 steps at 95% accuracy each and your total success rate is 60%. The hype ignores exponential decay.
If your system breaks because you changed an adjective, the problem isn't your prompt; it's your architecture. The mature approach is System Engineering.
Fine-tuning is effective for teaching form but terrible for injecting facts. The 'domain-specific fine-tuned model' pitch is mostly marketing.
Million-token context windows sound impressive. The reality: models struggle to retrieve information buried in the middle of long prompts.
The 'blackbox' narrative is convenient but wrong. AI models are sequences of math operations. Control the arithmetic, control the output.
LLMs predict the next token. That's it. They've mastered linguistic form without possessing communicative intent.
If code can do it, code should do it. There's a concerning number of AI automations that shouldn't be AI automations.
The industry conflates 'bigger' with 'better.' This ignores basic math. Running a cheaper model multiple times beats expensive single passes.
A comprehensive overview of common AI misconceptions and the factual realities that should guide engineering decisions.
The math on autonomous agents is brutal. Chain 10 steps at 95% accuracy each and your total success rate is 60%. The hype ignores exponential decay.
If your system breaks because you changed an adjective, the problem isn't your prompt; it's your architecture. The mature approach is System Engineering.
Fine-tuning is effective for teaching form but terrible for injecting facts. The 'domain-specific fine-tuned model' pitch is mostly marketing.
Million-token context windows sound impressive. The reality: models struggle to retrieve information buried in the middle of long prompts.
The 'blackbox' narrative is convenient but wrong. AI models are sequences of math operations. Control the arithmetic, control the output.
LLMs predict the next token. That's it. They've mastered linguistic form without possessing communicative intent.
If code can do it, code should do it. There's a concerning number of AI automations that shouldn't be AI automations.
The industry conflates 'bigger' with 'better.' This ignores basic math. Running a cheaper model multiple times beats expensive single passes.
Sıkça Sorulan Sorular
Ürünlerimiz, faturalandırma ve veri kapsamı hakkında cevapları bulun.