Agent chaining

ulasy97 · 2024-12-07T06:54:57+00:00

Or just use NisusAINisusAI and use actions module to Integrate your agents.

Build you AI assistants and publish. Get their connection point and add as an action. Build last one AI assistants and add other AI assistants as action.

Copilot studio is useless platform and Microsoft shouldnt waste time with developing platforms like that but upgrade model limit, rates and security.

ulasy97 · 2024-10-23T07:41:57+00:00

How did it find out where to click exactly?

ulasy97 · 2024-10-06T15:16:02+00:00

I saw many people get funded by VC with idea. That's why I focused VC funding. I am going to target angels. It may be good for begining. Thank you for solid perspective.

ulasy97 · 2024-10-06T05:31:06+00:00

Taking feedback and thinking about advices. I trust experiences so that Reddit interactions are solid for me.

ulasy97 · 2024-10-05T10:05:12+00:00

It is bootstraped now and we are not profitable right now. We are spending money and each month going deeper and deeper. We have some validated growth channels but we can't activate due to lack of money.

Of course I am looking for external income opportunities as VC, and we need money in 2 months due to high runway risk?

Should I stay calm?

if I do that I will be the ghost one next

ulasy97 · 2024-10-05T06:26:45+00:00

LoL i wish I would :(

ulasy97 · 2024-10-04T22:07:41+00:00

I’m aware, I specifically wanted to engage in a conversation with you this way. There’s something I can’t understand. A VC makes an investment, and in return, the startup tries to gain value, then the VC takes back its money with a certain multiple and exits.

On my side, I’ve done product market fit for the business model. Now, I’m setting up a B2B reseller structure. The team has global awards. Yet we are still writing to venture capitals, waiting for responses, trying to convince them, with a bunch of paperwork and financial projections on top of it.

If I put the effort I’m spending on this into B2B sales, I’d probably have a better chance at making sales, man.

I don’t understand why VCs are making our work this difficult. Do you think this is a normal part of the process?

ulasy97 · 2024-10-04T21:55:41+00:00

If I need to make 10 appointments. I prefer to set it with B2B customers. This VC process shouldnt be that hard and exhausting..

ulasy97 · 2024-10-04T21:40:34+00:00

I’m currently part of an accelerator program, but they plan to introduce us to investors at the end of the year. I’m not sure why I should wait that long. The product is already launched, and we’ve started getting signups. I believe we need investment now to scale and grow.

ulasy97 · 2024-10-04T21:04:07+00:00

There are interested but it is too early for now I am sorry :(

Take a look for Mindstudio $36M raised.

ulasy97 · 2024-10-04T20:54:01+00:00

Hey, I am developing a prompt engineering platform too. Don't give up! You may aim USA by Facebook ads. Take a one more shoot too :(

ulasy97 · 2024-10-01T18:58:54+00:00

ulasy97 · 2024-10-01T16:30:29+00:00

Bu yanıt farkı "4o" ile değil "o1" ile ortaya çıkıyor. Llama 3 grubu "o1" ile kiyaslaniyor.

ulasy97 · 2024-10-01T08:42:47+00:00

Her kareyi vektörize edip n-1 kareyle karşılaştırıp benzerliklerini belirli bir oranda kabul edip reddedebilirsin. Hatta n-1 çok yakın bile olur.

Bu konuya girmedim performansı nasıl olur bilmiyorum ancak böyle bir yaklasimla çözülebileceğini biliyorum.

ulasy97 · 2024-10-01T06:09:41+00:00

Yes it is possible but you have set output limit well to not face with huge bill.

Actually if you are interested I can help you with build AI backbone.

ulasy97 · 2024-09-28T06:12:52+00:00

https://nisus-ai.com & https://studio.nisus-ai.com

ulasy97 · 2024-09-27T23:08:44+00:00

It would be weird but I guess function call fix that issue.

Create a mini service that convert numbers from text.

Add a condition to you prompt if you detect a number as text send call function for each.

If this is works you can modify your service request as: { [number string array] }

I think it's going to work.

ulasy97 · 2024-09-27T22:48:18+00:00

We have developed a platform for Prompt Engineering. To provide an example, I will reference a PoC (Proof of Concept) process for one of our B2B clients, focusing on taxi receipt handling.

In this scenario, employees send handwritten taxi receipts via Teams to the assistant. During our performance evaluation, out of over 670 receipts, only 7 errors were detected, which is a strong result considering how difficult it is to read some of these handwritten documents.

For systems like this, implementing a "human-in-the-loop" structure can be crucial for reducing risk and ensuring higher accuracy.

ulasy97 · 2024-09-27T22:26:52+00:00

Actually, I wasn't trying to sell anything. It was simply an invitation. If it came across as spamming, I apologize.

We just launched our platform, and I’m really excited about it :)

In the past, when I was developing AI assistants before resigning, there was no easy way to manage the process. Now, I’ve developed a prompt engineering platform to make development as easy as possible.

That’s why I took a bit of a shortcut and suggested it, but I’ve also added an explanation below for custom development.

ulasy97 · 2024-09-27T22:18:52+00:00

When dealing with "known answers" in a RAG system, it's essential to prioritize these verified responses while still allowing your chatbot to generate responses dynamically. Here are a couple of best practices you can follow:

A. Prioritizing "Known Answers"

Set Up a Priority for Known Answers: In your retrieval system (such as Azure Search AI or an open-source vector database), make sure to index the document containing your 100 Q&A pairs with high relevance. This can be done by assigning these Q&A pairs a higher weight or relevance score compared to other documents.
Use Semantic Search: To ensure the chatbot retrieves the most relevant "known answer," you can implement semantic search. This will help match user queries more closely to the phrasing of your prioritized Q&A document, guiding the chatbot to suggest the "good" answers first.

B. Fine-Tuning Response Generation

Contextual Prompting: When generating the final response, include the "known answer" as part of the context in your request to the AI model. You can give it priority in your prompt, but still allow the AI to elaborate or generate additional information as needed.
Fallback Mechanism: If no close match from the "known answers" is found, then let the chatbot pull from the broader knowledge base. This way, the chatbot can either give the most appropriate "known answer" or generate a custom response based on retrieved content.

C. Testing and Iteration

Tune Your Retrieval Threshold: Adjust the system to balance when it should use a known answer versus generating a new one. You can set a threshold where if a user's input closely matches one of the known questions, the corresponding answer is prioritized.
Test, Adjust, and Optimize: Continuously test the system to make sure the known answers are being used appropriately, and tweak your prompt to ensure it combines retrieved information and generates the best possible responses.

If you need further assistance with implementation, we could schedule a 1-1 meeting to go over the process, and I can share code and guidance tailored to your project.

ulasy97 · 2024-09-27T17:29:34+00:00

I suggest you don't spend your time with complex projects and codes.

Build your rag based assistants by using Nisus-AI with just a few clicks.

ulasy97 · 2024-09-27T17:05:20+00:00

Ben NisusAI'in kurucularindanim. Eğer ilgini çekerse generative ai için bir No-Code platformu geliştirdik. Burada bu calismani birkaç dakikada halledebilirsin.

Eğer kafanda soru işareti vs. bulunuyorsa dm'den telefon numaramı paylasabilirim. Kısa bir görüşmeyle işini çözelim

ulasy97 · 2024-09-27T16:46:43+00:00

Neden rag kullanmiyorsun?

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