"Fine-tuning involves training the LLM on a smaller, task-specific dataset to adjust its parameters for the specific task at hand. This allows the LLM to leverage its pre-trained knowledge of the language to improve its accuracy for the specific task. Fine-tuning has been shown to drastically improve performance on domain-specific and task-specific tasks and lets LLMs adapt quickly to a wide variety of NLP applications." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)
"Transfer learning is a technique used in machine learning to leverage the knowledge gained from one task to improve performance on another related task. Transfer learning for LLMs involves taking an LLM that has been pre-trained on one corpus of text data and then fine-tuning it for a specific 'downstream' task, such as text classification or text generation, by updating themodel’s parameters with task-specific data." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)
"As the tech industry moves from non-generative models to generative models, it is shifting away from feature engineering, or creating features to model the data and experimenting with different hyperparameters to optimize performance. Generative models, and specifically LLMs, do not require feature engineering. Today, the core requirements are usually prompt engineering or building a RAG pipeline - skills that lie within the domain of AI engineers." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)
"Data drift manifests in several distinct ways. Input drift typically shows up as an increase in adversarial or malformed queries that deviate from the original training or design expectations. This can stress the system’s robustness and degrade output quality. Retriever drift occurs when the relevance of the documents returned by retrieval components declines, even if the retrieval algorithms and configurations remain unchanged. Similarly, embedding drift arises when the vector representations used to compare semantic similarity become less effective, causing retrieval systems to fail despite stable system parameters." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)
"LLM deployment failures often trace back not to the model itself, but to the prompts it receives. In production environments, prompts are rarely fixed, handcrafted snippets. Instead, they are dynamically generated, assembled from templates, and parameterized based on upstream data sources or evolving user state. This dynamism introduces complexity and variability that can subtly undermine the system’s performance if not carefully managed." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)
"In RAG methods, the AI model itself doesn’t actually 'remember' or learn the proprietary data directly. Instead, the proprietary data are stored separately in what’s called a vector database. When the model is asked a question, it first performs a quick search of the proprietary database, finds relevant pieces of information, and then uses these to generate its response. The model’s core parameters remain entirely unchanged and are never updated with this private data. In this sense, the model hasn’t learned or 'seen' your proprietary data in its internal parameters, it only temporarily consults it as a reference to formulate an answer." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)
"In 'traditional' ML, where models are predominantly trained for single problems like classifications or regressions, models require extensive training to adapt their weights or parameters to a new task. But you can use LLMs for a wide variety of tasks by changing just the prompt without changing the weights. Foundational models’ capability to adapt to new tasks based solely on examples or instructions they’re given in a prompt is called in-context learning. It emerges from training on diverse datasets that include various task formats and instructions. When given examples of a task within a prompt, the model identifies the underlying pattern and applies it to new instances. This process doesn’t modify the model’s weights, but rather, it leverages the model’s existing knowledge to perform new tasks." (Valliappa Lakshmanan & Hannes Hapke, "Generative AI Design Patterns: Solutions to Common Challenges When Building GenAI Agents and Applications", 2026)
"[...] LLMs raise serious concerns about ethics, bias and fairness, errors in reasoning, hallucinations, and misuse (e.g., misinformation and disinformation). These concerns are exacerbated by modern LLMs being both literal and figurative 'black boxes': Literal black boxes because many advanced AI systems are proprietary and the weights (trained parameters of the models) are not released to the public; and figurative black boxes because even the open-source AI models are so complicated that understanding them and developing safety guardrails has thus far proven extremely difficult." (Mike X Cohen,"50 ML Projects To Understand LLMs", 2026)
"RAG is a paradigm that combines the strengths of LLMs with the rich, often unstructured data stored in a lakehouse. Rather than asking an LLM to generate responses purely from its internal parameters and training data, where knowledge can be outdated or incomplete, RAG systems first retrieve relevant documents, records, or data slices from your lakehouse and then feed those pieces into the model as context for its generative step. The result is an AI that can speak confidently about the latest reports, proprietary datasets, or domain-specific knowledge you have stored without having to retrain the model each time your data changes." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026
"The foundational premise of RAG is that the most effective way to reduce hallucinations is to provide the LLM with the correct, explicit information needed to answer a query, thereby minimizing its need to rely on fallible parametric knowledge. Therefore, the quality, relevance, and accuracy of the retrieval step are the most significant factors in determining the truthfulness of the final output. Better retrieval is the most powerful antidote to hallucination. If the retriever fails to find the correct information, the generator is essentially left to guess, making hallucinations almost inevitable. The goal is to create a tight, unambiguous link between the user’s question and the evidence in the knowledge base." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)
"Traditional NLP systems rely on static, pretrained knowledge embedded within their parameters, limiting their responses to information available during training. In contrast, RAG systems dynamically access external knowledge bases in real time, enabling them to provide up-to-date and contextually relevant answers. This fundamental distinction leads to key differences in architecture, performance, and adaptability." (Saloni Garg et al, "RAG Artificial Intelligence: Retrieval-Augmented Generation in Generative AI", 2026)
"Transformers dominate language for several reasons: their autoregressive framework provides a straightforward training objective (predict next token), self-attention captures long-range dependencies across thousands of tokens, parallel training processes entire sequences simultaneously (unlike RNNs’ sequential bottleneck), and scaling behavior produces emergent capabilities at billions of parameters. These properties make transformers uniquely suited for the language understanding and generation tasks central to enterprise AI applications [...]" (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)
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