A Recap of Generative AI Tools, Frameworks, and Buzzwords in 2023

As we step into a new year, lets reflect on the dynamic landscape of generative AI and explore the tools, frameworks, and buzzwords that dominated conversations in the past year. The rapid advancements in artificial intelligence continue to shape industries and pave the way for innovative solutions. In this article, lets take a closer look at the key players that made headlines in the world of generative AI in 2023.

GPT-4:

Undoubtedly, one of the most anticipated releases of 2023 was the next iteration of OpenAI's Generative Pre-trained Transformer, GPT-4. Building on the success of its predecessor, GPT-3, this powerful language model raised the bar with even more parameters, improved natural language understanding, and enhanced generative capabilities.

LLM frameworks (Langchain, Llamaindex, Semantic Kernel etc)

LLM frameworks attracted lots of attention with the advent of various open source models in order to orchestrate flow, integrate with different agents, add memory, prompt templating etc. Another talking point around the framework were the UI based configuration tool (e.g. Langflow) enabling end user to quickly configure a workflow around Large Language Models

Python Library extension (pandasai, scikit-llm, etc.)

The idea of combining Generative AI capabilities with popular data analytics library started and packages like pandasai, scikit-lllm saw the day of light where we can leverage LLM capabilities like Zero shot learning, vectorization in your analysis or can make your dataframes conversational.

RAG (Retrieval Augmented Generation)

As the need of fine tuning and contextualizing LLMs with internal data took momentum, a new framework RAG (Retrieval Augmented Generation) got lots of traction where Data can be generated with background on internal or private data without retraining the entire Large Language Model. It proved to be a cost efficient way to contextualize responses of LLMs to a targeted set of actions.

LLM Evaluation parameters and framework

With many models getting released each day, a common framework was required to evaluate and score the Large Language Models. Evaluation frameworks like HELM (Holistic Evaluation of Language Models) and benchmark parameters like HellSwag, MMLU, BoolQ, TruthfulQA, Winograde etc were trending in the AI community

Responsible AI

With many startups mushrooming around Generative AI, Responsible AI picked up the talk of town referring the ethical and responsible development of (Generative) AI applications. It revolved around principles like Privacy, Security, Fairness, Explainability, Transparency etc. so as to have applications getting developed in ethical and responsible way

The generative AI landscape in 2023 was marked by significant advancements in tools, frameworks, and methodologies and above are just few of the buzz words which got picked up in 2023 , although some of them were present since long. As we step into the future, these developments pave the way for exciting possibilities, furthering the impact of generative AI across various industries.