@AbhinavXJ 's post shows why prompt injection matters.π
A malicious prompt can make an AI ignore instructions, expose sensitive data, or take unintended actions.
As AI agents gain access to tools, APIs and private data, guardrails are essential.
#AI#LLM#PromptInjection
Chain-of-thought made LLMs better at breaking complex problems into smaller steps, planning, and reasoning toward an answer.
But there was a catch: reasoning with no access to external data can still produce confident hallucinations.
Hence comes ReAct into the picture
#LLM#AI
Round 2 π
Qualified for Round 2 of the Adobe University Hackathon 2026!
Excited to see what the next round brings.
#AdobeHackathon#Hackathon#BuildInPublic
Ever wondered why GPT models doesn't wait to finish before showing its answer?
Because waiting feels broken.π
Streaming sends tokens as they're generated, making AI feel responsive and conversational instead of making you stare at a blank screen.
#LLM#GenAI#UX
The previous post ended at embeddings. That's only half the story.
LLMs generate tokens one at a time. Recomputing every previous token for each new one would be painfully slow.
That's why they use KV Cacheβreusing past attention computations instead of recalculating them.
#LLM
Most people think LLMs read English...
They don't.
Your prompt is tokenized, every token becomes a numerical embedding, and that's what the model processes before predicting the next token.
The chat interface hides a lot of interesting engineering.
#LLM#GenAI#LearnInPublic
Finished building a mini project while learning LangGraph.
Built an AI Article Generator using an iterative workflow: Generate β Evaluate β Optimize.
Deployed it so anyone can try it.
Building projects makes these concepts click far better than reading docs.
#LangGraph#LLM
Finally wrapped up the core LangGraph workflows...
Sequential β Parallel β Conditional β Iterative β
Iterative stood out the most. Having an agent generate, evaluate, improve, and repeat feels much closer to real problem solving.
Time to build.
#LangGraph#AI#Python
@Amoghjaiswal_ Yea it amazes me too how attention lets the model weigh every single token's relevance to others, turning each embeddings into rich contextual understanding for much better generations
Most people think LLMs read English...
They don't.
Your prompt is tokenized, every token becomes a numerical embedding, and that's what the model processes before predicting the next token.
The chat interface hides a lot of interesting engineering.
#LLM#GenAI#LearnInPublic
The word "bank" can mean a financial institution or the side of a river.
A token embedding can't tell the difference.
Transformers refine embeddings using surrounding context until each token has the right meaning.
That's where much of an LLM's intelligence comes from.
#LLM#AI