Prompt Design Chain of Thought Few-shot Prompting Structured Outputs JSON Mode Function Calling Tool Use Prompt Testing Prompt Optimization System Prompts
33 posts
In the early days of Large Language Model (LLM) adoption, building an application often felt less like software engineering and more like alchemy. Developers would tweak a system prompt, hit "run," and hope for the best. Today, as LLMs move from experimental chatbots to critical production compon...
As Large Language Models (LLMs) become the backbone of complex enterprise applications, the limitation of context windows has emerged as a critical bottleneck. While models like GPT-4 or Claude 3 boast massive context capacities (128k+ tokens), naive strategies of simply stuffing entire documents...
As developers integrate Large Language Models (LLMs) into production environments, we quickly encounter a significant bottleneck: the model's inability to handle multi-step reasoning effectively. While LLMs are incredibly powerful at pattern matching and retrieval, they often struggle with logica...
As Large Language Models (LLMs) become integral to enterprise applications and consumer products, the security landscape surrounding them has evolved rapidly. While traditional software vulnerabilities like SQL injection are well-documented, LLMs introduce a unique attack vector known as Prompt I...
For years, Large Language Models (LLMs) were treated as black boxes that generated text based on probability. While impressive, this approach had a fundamental limitation: they lacked access to real-time data and could not perform actions. Enter Tool Use (also known as Function Calling or Action ...
Large Language Models (LLMs) possess remarkable generative capabilities, but they are inherently static. They cannot check live inventory, fetch real-time weather, or query internal corporate databases without assistance. This limitation is where Function Calling (or Tool Use) becomes essential. ...
Artificial Intelligence has evolved beyond static chat interfaces. Today, the frontier of LLM development lies in Agentic Workflows. Unlike traditional prompting where the model answers directly, agentic systems reason, plan, and execute actions using external tools. This paradigm shift allows AI...