When you do make that LLM call, it's all about context engineering. To get a good answer back, you need the right context at the right time sent to the right model.
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Pre-processing Information for LLM Problem Solving
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You need to pre-process all available information, prompts, and user input so the LLM can easily and reliably solve the problem. This is the most fundamental skill in working with LLMs.
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Breaking Down Components: When to Use LLMs in Software Engineering
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The solution is simpler than most frameworks make it seem. Here's the approach that actually works: – Break down what you're building into fundamental components
– Solve each problem with proper software engineering
– ONLY use LLMs when deterministic code fails -
LLMs Should Reason With Context, Not Make All Decisions
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But in reality, you don't want your LLM making every decision. You want it handling the one thing it's good at – reasoning with context – while your code handles everything else.
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Simple Custom AI Blocks Beat Agent Frameworks
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Most successful AI applications I've seen are built with simple, custom building blocks, not agent frameworks. This is because most effective "AI agents" aren't actually that agentic at all.
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Building Cost-Effective AI Agents Without Expensive LLM Calls
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Making an LLM API call is the most expensive and most dangerous operation in modern software development. While incredibly powerful, you want to avoid it at all costs and only use it when absolutely necessary. Here's how to build AI agents that actually work:
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Zenith and Summit: GPT-5 Model Specialization Roles
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It feels like Zenith might be the creative / normal part of GPT-5 and Summit is where the coding tasks would be routed. I haven't come across Zenith in any coding tasks, but in regular questions it tends to come up once in a while
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Grid Aware ML: Electromagnetic Systems and Big Data Analytics
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Grid Aware Electromagnetic Machine Learning! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Electromagneti
c-Wave
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Machine Learning Event Draws 674 Participants Worldwide
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1974 Machine Learning! 674 Participants! @DataSciConnect #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode http://
geni.us/DSCK-12 -
Mastering Transformers: Essential Guide for Data Scientists
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Mastering Transformers! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #Books #100DaysofCode https://
geni.us/Mastering-TFS