Continuous Learning LLM Prompt Engineering 
AWS Machine Learning Blog
FEBRUARY 21, 2025
Fine-tuning a pre-trained large language model (LLM) allows users to customize the model to perform better on domain-specific tasks or align more closely with human preferences. Continuous fine-tuning also enables models to integrate human feedback, address errors, and tailor to real-world applications.
AI News
SEPTEMBER 27, 2024
” He notes it’s powered by “a compound AI system that continuously learns from usage across an organisation’s entire data stack, including ETL pipelines, lineage, and other queries.”
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AWS Machine Learning Blog
APRIL 24, 2024
To increase training samples for better learning, we also used another LLM to generate feedback scores. We present the reinforcement learning process and the benchmarking results to demonstrate the LLM performance improvement. of overall responses) can be addressed by user education and prompt engineering.
Unite.AI
JANUARY 11, 2024
Synthetic Data Generation: Prompt the LLM with the designed prompts to generate hundreds of thousands of (query, document) pairs covering a wide variety of semantic tasks across 93 languages. Model Training: Fine-tune a powerful open-source LLM such as Mistral on the synthetic data using contrastive loss.
TheSequence
FEBRUARY 16, 2025
We discuss the potential and limitations of continuous learning in foundation models. The engineering section dives into another awesome framework and we discuss large action models in our research edition. By continual pre-training on Hephaestus-Forge, the resulting model, Hephaestus, shows improved agentic capabilities.
Unite.AI
AUGUST 28, 2023
LLM & Agents : At the core, the LLM processes these inputs, collaborating with specialized agents like Auto-GPT for thought chaining, AgentGPT for web-specific tasks, BabyAGI for task-specific actions, and HuggingGPT for team-based processing. ” BabyAGI responded with a well-thought-out plan.
Unite.AI
FEBRUARY 21, 2025
How Reinforcement Learning Enhances Reasoning in LLMs How Reinforcement Learning Works in LLMs Reinforcement Learning is a machine learning paradigm in which an agent (in this case, an LLM) interacts with an environment (for instance, a complex problem) to maximize a cumulative reward.
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