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NEW

Examples of Reinforcement Learning in LLM Apps: From RLHF to Safer Business Outputs

Watch: Reinforcement Learning from Human Feedback (RLHF) Explained by IBM Technology In our experience, preference-tuned models can outperform much larger unaligned counterparts on human preference evaluations — alignment work often matters as much as raw scale when the target is human preference.…
Thumbnail Image of Tutorial Examples of Reinforcement Learning in LLM Apps: From RLHF to Safer Business Outputs
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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Reinforcement Learning in Python for LLM Fine-Tuning: Build a Simple RLHF PPO Pipeline

Short version: RLHF with PPO starts from a supervised-fine-tuned model and nudges it toward preferred outputs. You train a reward model on pairwise comparisons, then use PPO to update the policy while keeping it tethered to the starting checkpoint. The training loop generates completions, scores…
Thumbnail Image of Tutorial Reinforcement Learning in Python for LLM Fine-Tuning: Build a Simple RLHF PPO Pipeline
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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NEW

Python dotenv for LangChain Agents: Safe .env Patterns for RAG, Tools, and Local LLM Apps

Watch: Build a RAG (Retrieval-Augmented Generation) System from Scratch | Python + Vector DB + LLM by CodeVisium Python-dotenv gives local LangChain projects a simple way to keep credentials out of source code while still making them available through os.environ. It works well for solo prototypes,…
Thumbnail Image of Tutorial Python dotenv for LangChain Agents: Safe .env Patterns for RAG, Tools, and Local LLM Apps
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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Let Your Internal AI Agent Rewrite Its Own Prompts From Failed Runs

When you build AI agents that rewrite their own prompts after failed runs, you're addressing a gap that's been draining engineering time: the hidden orchestration tax of manual prompt iteration. The shift matters because every ambiguity in an agent prompt compounds across steps, turning vague…
Thumbnail Image of Tutorial Let Your Internal AI Agent Rewrite Its Own Prompts From Failed Runs
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
•Last Updated:
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Grep Beats Embeddings for Some AI Coding Tool Tasks: Use Deterministic Search Before You Build RAG

Watch: Is RAG Still Needed? Choosing the Best Approach for LLMs by IBM Technology Short version: A SWE-Bench Verified agent using only grep and find reached the top of the leaderboard without any semantic indexing. A single exact-name query can use far fewer tokens with grep than broad embedding…
Thumbnail Image of Tutorial Grep Beats Embeddings for Some AI Coding Tool Tasks: Use Deterministic Search Before You Build RAG
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
•Last Updated:
00
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