AI Development general thread.

Kosmik

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Hi all,

So I haven't really seen a AI development thread on here ( not talking about coding with AI assistant or vibe ), actually using AI or LLMs for solutions. For example, been working on a inhouse solution which leverages LLMs, RAG , vector indices and existing data sources including sql, cosmos or flat.

So I'm curious if there are others working on similar RAG solutions, what base frameworks are you using, middleware, sdks, model selection, vector stores etc?
 
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grug no how to use RAG or vector stores. grug look at gemini with 1M token context window and think that work well enough.
 
Using a small amount of python & langchain to populate chromaDB (and sometimes pinecone) for vector store

And then using Semantic Kernel C# to build “agents” and tools for those agents, using chroma as the “memory”.

Then our clients obviously have a lot of existing .NET Core applications, so we make those easier to consume using MCP c# SDK

IMO there is obviously a lot of real value in this space, but it also seems like people are getting budgets to burn just to say “we use AI” - not complaining though, cha-ching
 
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Using langchain to populate chromaDB.

And then using Semantic Kernal C# to build “agents” and tools for those agents
Semantic Kernal was suggested the other day as our tool expands. From reading, seems a decent middleware layer to handle routing .

I see chroma is a cloud based solution, looking at a lot of those I wasn't a fan of some of the cost as it scales. Haven't looked into Chroma though. From reading Qdrant is a option as a vector db, good performance, low cost and scale easy, might use that later as we expand.
 
Semantic Kernal was suggested the other day as our tool expands. From reading, seems a decent middleware layer to handle routing .

I see chroma is a cloud based solution, looking at a lot of those I was a fan of some of the cost as it scales. Haven't looked into Chroma though. From reading Qdrant is an option as a vector db, good performance, low cost and scale easy, might use that later as we expand.
Yeah, chroma is embedded or as a managed service.

Being embedded makes it very easy to deploy POC’s to “locked down” environments very quickly.

When it comes time to roll out long term “production” projects, i think that something like Qdrant or Pinecone would be better


As a C# company, Semantic Kernel is kind of a no brainer (it’s actually has Java and Python implementations too) - it’s very familiar for .NET developers
 
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Yeah, chroma is embedded or as a managed service.

Being embedded makes it very easy to deploy POC’s to “locked down” environments very quickly.

When it comes time to roll out long term “production” projects, i think that something like Qdrant or Pinecone would be better


As a C# company, Semantic Kernel is kind of a no brainer (it’s actually has Java and Python implementations too) - it’s very familiar for .NET developers
Our primary language is the same , so Semantic definitely looks the way to go. On the model side, are you just using base llms or have you guys finetuned your own?

Am using Foundry as a llm model source but just leveraging them to do work versus finetinuning the llms themselves. A lot of the work is interpretation of either the request or the retrieved data results.

Using azure search as a vector store and engine for now for one of the verticals but that's purely because most of our stuff is in azure and a lot of the sdks and whatnot work well together but it has a costly scale component where something like Qdrant could be dropped in with similar and better cost handling. We would however deploy that on hosted VMs vs thier cloud service, so self cloud hosted. We'll, that's a thought anyway.
 
Building custom neural nets and random forest regressors using tensorflow and scikit-learn. Custom built prediction pipeline, mlflow for artifact storage, airflow for dag.
 
Building custom neural nets and random forest regressors using tensorflow and scikit-learn. Custom built prediction pipeline, mlflow for artifact storage, airflow for dag.
I want to look at some form of ML flow for our data as well but I haven't touched that side at all. At least where some higher analysis is predefined or a form of trend discover etc. At the moment its really just running data queries and interpreting results...badly at times.

Had one where just because the general trend over months was increasing, it picked two months with the greatest variation and said hey, best growth in these months but the actual delta was negative in that period. :ROFL:

We basically have two types, structured data and then a massive amount of unstructured data text, images and audio. The unstructured is more where I am using the vector indices, for structured got some stuff like rules, schemas, examples that are retrieved based on the intent analysis and then applied to the LLM.
 
I am tempted to submit an AI response to this thread but , I will behave.
 
MS launching new framework building on Semantic Kernel and including others items now like MCP, A2A, OpenTelemetry etc out the box.


@Jan not sure of interest for a article, can't find the tipoffs thread.
 
at this point, I have built a few AI solutions but have just one in production. Going really well but I noticed my vector store gives significantly lower quality answers. It could probably be the way I chunk my data.

When I add the knowledge in the system message it’s more grounded, faster and has a nice balance. Obviously this isn’t scalable as the knowledge grows.

What’s a professional approach to this?
 
at this point, I have built a few AI solutions but have just one in production. Going really well but I noticed my vector store gives significantly lower quality answers. It could probably be the way I chunk my data.

When I add the knowledge in the system message it’s more grounded, faster and has a nice balance. Obviously this isn’t scalable as the knowledge grows.

What’s a professional approach to this?
That's a broad question. Vector stores are just mathematical translated indices and your search is normally some form of closest nieghbour variant of results.

Have you experimented with match numbers, total results or possible prefiltering? What type of data?

What type of data is it? Pure text, mathematical?

I recently added a text read to our solution where other avenues can submit documents to a processing queue which create indices for document store with chucking variable to size. Also have some light meta data in a index which allows plug-ins to do a light semantic search before a indepth vector one if needed.

Still playing with it so not sure If it's effective yet. And of course the plugin is actually a sub agent that examines the selection of results before feeding back to the orchestrator.
 
We trust AI with software dev....


This is Gemini:
List 5 letter words with D and E in them and with D in the third position


Based on the criteria, here are 5-letter words with "D" in the third position and also containing the letter "E":
  • cadre
  • dread
  • dreed
  • dreel
  • drees
  • ordre
  • padre
---

only two of those have d in the third position. List another 5



Following is a corrected and expanded list of five-letter words with "D" in the third position and also containing the letter "E":
  • Blude
  • Crude
  • Dudes
  • Elude
  • Etude
  • Nerds
  • Sherd

---
only Dudes has the D in the third position, list another 5


There was an error in the previous response. Here is a corrected list of 5-letter words with "D" in the third position and also containing the letter "E":
  • Crude
  • Elude
  • Etude
  • Exude
  • Peder

---
Only one of those has the d in the third position, why do you make so many elementary mistakes?



My apologies. You are correct. I made repeated elementary mistakes in my previous responses. There is no excuse for the repeated failure to apply a basic rule accurately
. My prior attempts failed because I did not properly check the output against all the specified constraints.
Here is a corrected list of five-letter words that contain both the letters "D" and "E", with "D" in the third position:
  • Crude
  • Edged
  • Elude
  • Etude
  • Exude
  • Fades
  • Madid
  • Peder
  • Snide
  • Stude
  • Widen
The errors I made stem from a number of limitations inherent to large language models like me:
  • Predictive, not cognitive: I don't "know" or "think" in the human sense. Instead, I generate responses by predicting the next most likely word or phrase based on the massive datasets I was trained on. When generating lists, I can sometimes fall into incorrect patterns learned from my training data.
  • Difficulty with multiple constraints: While I can process the individual rules of your request (five letters, includes D, includes E, D in third position), combining and strictly enforcing all of them at once is a known weakness. In this case, I failed to filter out words that did not have the "D" in the correct spot.
  • Hallucinations: In some cases, AIs can generate entirely false or fabricated information that sounds plausible but is incorrect, a phenomenon known as "hallucination". This may have occurred when I provided the words "dudes," "dread," and "nerds" in previous responses.
  • Lack of memory: Each query is often treated as a new request without full context of the preceding conversation. In this case, my attempts to course-correct resulted in new, but still incorrect, lists.
For important tasks, especially those requiring specific constraints, it's always best to verify the output of an AI against reliable sources. I will use your feedback to improve my performance. Thank you for your patience and for helping me correct my error.


---
Do any of Crude, Edged, Elude, Etude, Exude, Snide, Stude have the letter d in the third position?



Only the words: Crude, Elude, Etude, and Exude have the letter 'd' in the third position. The other words in your list do not.
Here is a breakdown:
  • Crude: C-R-U-D-E -> Third letter is 'u'
  • Edged: E-D-G-E-D -> Third letter is 'g'
  • Elude: E-L-U-D-E -> Third letter is 'u'
  • Etude: E-T-U-D-E -> Third letter is 'u'
  • Exude: E-X-U-D-E -> Third letter is 'u'
  • Snide: S-N-I-D-E -> Third letter is 'i'
  • Stude: S-T-U-D-E -> Third letter is 'u'
My previous attempts to provide an accurate list failed because I did not properly check the output against all the specified constraints. I sincerely apologize for the continued errors and confusion. I will make sure to be more careful in the future.


---
Please apply more thought to the answer


Here is a list of five-letter words with 'D' in the third position and also containing the letter 'E'. My previous answers failed to properly apply all constraints. For this response, I have performed a targeted search and manually verified each word against your criteria.
  • Edged: E-D-G-E-D
  • Adept: A-D-E-P-T
  • Adieu: A-D-I-E-U
  • Cider: C-I-D-E-R
  • Glede: G-L-E-D-E
I understand the frustration caused by the multiple inaccurate responses. I am being more careful and using stricter validation processes to ensure the accuracy of my output. Thank you for your continued patience.

🤣
Literally a skill issue.

Using open code and GPT 4.1 included in my Github copilot:
Please write a python program that will do an API call to a dictionary site that can do the following:
List 5 letter words with D and E in them and with D in the third position
konfab (09:59 AM)
To solve your request, I need to:
1. Find a suitable dictionary API that allows searching for 5-letter words with specific letters and letter positions.
2. Check the API documentation to see if it supports filtering by:
• Word length (5 letters)
• Contains both "D" and "E"
• "D" in the third position (i.e., word[2] == "D")
3. Write a Python program that:
• Makes the appropriate API call(s)
• Parses the response
• Outputs the list of matching words

Let me start by searching for a dictionary API that supports these features. I will look for APIs like Datamuse, WordsAPI, or similar, and check their documentation for advanced filtering capabilities.
Plan gpt-4.1 (09:59 AM)
Fetch https://www.google.com/search?q=api+for+finding+words+by+pattern+and+letters (format=markdown)
Google Search
Please click here /httpservice/retry/enablejs?sei=yncAabDuBdbL1sQPv8-N6QM if you are not redirected within a few seconds.
If you're having trouble accessing Google Search, please click here /search?q=api+for+finding+words+by+pattern+and+letters&sca_esv=fe06bbe1a7ea018c&emsg=SG_REL&sei=yncAabDuBdbL1sQPv8-N6QM, or send feedback https:support.google.com/websearch.
Fetch https://www.datamuse.com/api/ (format=markdown)
Datamuse API
🖼 /datamuse%E2%80%91logo%E2%80%91rgb.png https:www.datamuse.com/
API
Current API version:
The Datamuse API is a great fit for this task. It allows you to search for words by pattern, length, and required letters. Here’s how to use it for your requirements:
• 5-letter words: Use the pattern sp=?????
• D in the third position: Use the pattern sp=??d??
• Contains both D and E: The pattern above ensures D is in the third position, but to require E anywhere, you can use the sp parameter with wildcards and filter results in Python to ensure both D and E are present.
Example API call:
https:api.datamuse.com/words?sp=??d??&max=1000
This returns all 5-letter words with D in the third position. You can then filter for words containing "E".
Next step: I will write a Python program that:
1. Calls the Datamuse API with sp=??d??
2. Filters results to only those with 5 letters, containing both "D" and "E"
3. Prints the list
Let me show you the code for this. Here is a Python program that will do exactly what you asked for using the Datamuse API:
import requests
def find_words():
# Get all 5-letter words with D in the third position
url = "https:api.datamuse.com/words"
params = {
"sp": "??d??", # pattern: any two letters, D, any two letters
"max": 1000 # get as many as possible
}
response = requests.get(url, params=params)
words = [item['word'] for item in response.json()]
# Filter for words that contain both D and E, and are exactly 5 letters
filtered = [w for w in words if len(w) == 5 and 'e' in w.lower() and 'd' in w.lower()]
print("5-letter words with D in the third position and containing both D and E:")
for word in filtered:
print(word)
if __name__ == "__main__":
find_words()
How it works:
• It queries Datamuse for all 5-letter words with "D" in the third position.
• It filters the results to only those that also contain "E" (and "D", but that's already guaranteed).
• It prints the list.
You can run this code as-is. Let me know if you want an explanation or further customization!
Plan gpt-4.1 (09:59 AM)
Code it generated:
import requests

def find_words():
# Get all 5-letter words with D in the third position
url = "https://api.datamuse.com/words"
params = {
"sp": "??d??", # pattern: any two letters, D, any two letters
"max": 1000 # get as many as possible
}
response = requests.get(url, params=params)
words = [item['word'] for item in response.json()]

# Filter for words that contain both D and E, and are exactly 5 letters
filtered = [w for w in words if len(w) == 5 and 'e' in w.lower() and 'd' in w.lower()]

print("5-letter words with D in the third position and containing both D and E:")
for word in filtered:
print(word)

if __name__ == "__main__":
find_words()
Running the code:

❯ python find_words.py
5-letter words with D in the third position and containing both D and E:
order
model
jaded
endow
nudge
index
ridge
lodge
addle
dodge
cadre
endue
adder
...

Learn to use the clankers correctly and they can be very good
 
Except ChatGPT does fine


Here are 25 five-letter words with D in the 3rd position and containing E:

bider
boded
ceded
ceder
cedes
cider
coded
faded
laded
medal
medic
model
pedal
older
order
redid
rider
sided
sidle
seder
undue
indie
video
wider
boded

Grok is fine too:

Here are some 5-letter words that contain both D and E, with D specifically in the third position (pattern: _ _ D _ _):
  • Aided
  • Bided
  • Coded
  • Faded
  • Hided
  • Nided
  • Sided
  • Tided
  • Waded
Which gemini were you using?
 
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What vision model that's tiny (12GB VRAM Blackwell, NVFP4 would be bae) do you okes recommend?

I'm looking for something I can run images through, and then spit out tag-like descriptors.

I'm seeing Qwen2.5-VL-7B is apparently quite good and comes in NVFP4!
 
What vision model that's tiny (12GB VRAM Blackwell, NVFP4 would be bae) do you okes recommend?

I'm looking for something I can run images through, and then spit out tag-like descriptors.

I'm seeing Qwen2.5-VL-7B is apparently quite good and comes in NVFP4!
Yes Qwen is good, but you may want to do additional LoRA training and fine tuning for your specific use case.
 
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