Tag
GPT-4 Turbo, released in November 2023, expanded GPT-4's context to 128,000 tokens and cut the input price threefold, down to 10 dollars per million tokens. GPT-4o now beats it on price, speed and answer quality, but Turbo still holds up in stable production apps, contracts pinned to a specific version, and deterministic tests that depend on its exact behaviour.
Outlines, Guidance and Instructor force the model to emit valid JSON in the generation step itself. When they beat retries and function calling.
Mixtral 8x22B is Mistral AI's Mixture of Experts model released in April 2024: 141B total parameters but only 39B active per token, an unrestricted Apache 2.0 licence, and multilingual performance ahead of Llama 3 70B in Spanish, French, Italian, and German. Production serving needs datacenter-class GPUs.
Claude 2, launched by Anthropic in July 2023, offers a 100,000-token context window and safety grounded in Constitutional AI. Against GPT-4 it wins on long-document analysis and wide-context code; GPT-4 remains ahead on complex mathematical reasoning and its tooling ecosystem.
LangChain is a Python framework that unifies building LLM applications: prompt templates, retrievers over vector databases, function-calling agents, and conversational memory. It earns its keep in fast prototypes and multi-model systems, but for a single well-defined production use case, direct code usually stays more maintainable.
Function calling converts the LLM into a component that returns structured data. How it works, real use cases, and common production pitfalls.
Five months after launch, GPT-4 excels at chained reasoning, technical writing, and medium-complexity code, but still fails at arithmetic, post-cutoff information, and cross-conversation consistency. Claude 2 wins on long context; LLaMA 2 wins on cost and privacy.
Google launched Bard in February 2023 with PaLM 2 as its answer to ChatGPT, unveiling the model in May the same year in four sizes: Gecko, Otter, Bison, and Unicorn. PaLM 2 competes with GPT-3.5 and GPT-4 on benchmarks like MMLU and BIG-bench, but Google's real edge is Workspace integration, not the model itself.
Fine-tuning your own LLM pays off in three cases: you need a very specific style or voice, a rigid structured output format, or you want lower cost and latency from a small specialised model. LoRA and QLoRA have cut the GPU cost, but preparing data and running the model in production are still expensive. For everything else, RAG and prompt engineering are usually enough.
Cerebras-GPT is a family of 7 open-source language models, ranging from 111 million to 13 billion parameters, trained by Cerebras Systems on its CS-2 processors with the standard GPT-3 architecture. Released on Hugging Face and GitHub under the Apache 2.0 license, they suit fine-tuning, research, and local inference, though they understand only English.
Microsoft 365 Copilot integrates large language models into Word, Excel, Teams, and Outlook to draft, summarise, and analyse data without leaving the usual workflow. It operates inside the tenant's data graph, respects the organisation's existing permissions, and never uses that data to train the base OpenAI or Anthropic model.
GitHub Copilot is an AI-powered coding assistant that suggests code as you type, built into VS Code, JetBrains, and Neovim. It speeds up repetitive tasks and API exploration, but it also generates plausible code with real errors: studies document security vulnerabilities in a meaningful share of its suggestions, so every suggestion still needs human review.
