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Retrieval Augmented Generation (RAGs)
An introduction to RAGs.
Jun 2
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BowTied_Raptor
4
April 2026
Defensive Prompt Engineering
Why prompt attacks stop being a toy problem the moment your model touches real data, tools, or users
Apr 23
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BowTied_Raptor
1
March 2026
Best practices for prompt engineering
How to write prompts that are clearer, covering concepts like zero-shot, decomposition, etc...
Mar 29
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BowTied_Raptor
3
AI as a Judge
Fast, cheap, and surprisingly useful. Also easy to misuse if you forget that the “judge” is just another model with its own prompt, biases, and sampling…
Mar 10
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BowTied_Raptor
3
February 2026
Perplexity is not your KPI
The metric that trains language models is useful, but it can lie to you the moment you start shipping LLM products, or start building AI agents.
Feb 25
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BowTied_Raptor
1
Why LLMs change their mind
How you can build trust at scale, with these LLM models (and fight against LLM Hallucinations)
Feb 14
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BowTied_Raptor
4
January 2026
The 2 Dials that decide everything with foundation models
Architecture chooses what the model can express. Scale chooses what it can *afford* to learn.
Jan 26
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BowTied_Raptor
3
The real differentiator behind foundation models
Architectures usually get all of the headlines, but, it's the data (and how you sample it) which decides what the model actually learns.
Jan 13
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BowTied_Raptor
5
The AI Engineering Stack
Building AI products is less about “the model” and more about decisions, feedback loops, and boring engineering that doesn’t break.
Jan 9
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BowTied_Raptor
6
December 2025
Planning AI Applications
How to pick the right use case, set measurable goals, and avoid the “cool (but useless) demo” trap
Dec 18, 2025
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BowTied_Raptor
1
The Rise of AI Engineering
Foundation models changed the job. Here’s what an “AI Engineer” actually does.
Dec 12, 2025
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BowTied_Raptor
5
November 2025
Generative Deep Learning 2
Neural style transfers, and coding your very own Generative Adversarial Network
Nov 29, 2025
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BowTied_Raptor
2
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