mljourney The Fine-Tuning Temptation Fine-tuning is the most over-prescribed solution in LLM application development. When a model produces outputs that do not match expectations, the intuitive response is to train it on better examples. But in the majority of cases, the problem is not that the model lacks the capability — it is that the instructions … Read more
Why Model Selection Is Harder Than It Looks The LLM market in 2026 offers dozens of capable models across every price point, capability level, and deployment mode. Benchmark leaderboards exist, but they measure performance on standardised academic tests that may have little correlation with performance on your specific task. A model that ranks first on … Read more
The Decision That Shapes Everything Downstream Choosing between open-source and proprietary LLMs is not primarily a technical decision — it is a strategic one that determines your cost structure, your data governance posture, your dependency on external vendors, your ability to customise, and the ceiling on what you can build. Get it wrong and you … Read more
Beyond Code Completion: The Full Picture Most discussions of LLMs in software engineering focus on code completion tools like GitHub Copilot. These are genuinely useful, but they represent only a fraction of where LLMs are transforming how engineering teams work. The broader impact spans the entire software development lifecycle: requirements analysis, architecture decision-making, code review, … Read more
Why Sales Is a Natural Fit for LLMs Sales work is built on three capabilities that LLMs directly augment: knowing your prospect well, communicating persuasively, and managing a complex pipeline efficiently. Researching a company before a call, drafting personalised outreach, preparing tailored proposals, and writing follow-up communications are all time-intensive text tasks. The average sales … Read more
How LLMs Are Reshaping Financial Analysis Financial analysis is one of the most language-intensive professional functions outside of law. Earnings calls, annual reports, regulatory filings, research notes, credit memos, and board presentations are all primarily text — and large language models are transforming how analysts produce, consume, and synthesise each of them. The productivity gains … Read more
The Privacy Stakes in LLM Applications Every LLM API call is a data transfer. The text sent in your prompts — user queries, document contents, conversation history — travels to a third-party server, is processed by a model, and generates a response. For most types of content this is unremarkable. For content that includes personal … Read more
Why HR Is One of the Most Promising — and Sensitive — Areas for LLMs Human resources sits at the intersection of two things LLMs are exceptionally good at — processing large volumes of text and drafting personalised communications — and one thing that demands extreme care: decisions that affect people’s livelihoods and that carry … Read more
Why Legal Is One of the Best Fits for LLMs Legal work is disproportionately language-intensive. Contracts, briefs, research memos, due diligence reports, regulatory filings, and client communications are all primarily text — the core medium in which LLMs operate at their strongest. Unlike industries where the value is in physical processes, manufacturing, or numerical computation, … Read more
