RAG v/s Fine-Tuning v/s Prompt Tuning | Advanced Prompt Engineering | RAG OpenAI | Simplilearn
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In this comprehensive Simplilearn video, we dive into key AI tuning techniques, starting with RAG (Retrieval-Augmented Generation), which enhances pre-trained language models by integrating external data retrieval to improve response accuracy. We then explore Fine-Tuning, where pre-trained models are adjusted using specific datasets to optimize performance for targeted tasks. Next, we cover Prompt Tuning, a more lightweight approach that focuses on refining input prompts rather than retraining models. Throughout the video, we compare these methods based on their approach, purpose, use cases, and data dependency, while also highlighting their similarities, such as their shared goal of optimizing AI outputs. Whether you're new to AI or an experienced professional, this video provides a detailed look into these essential techniques, helping you understand when and how to apply them effectively. Don't forget to like, share, and subscribe for more AI insights from Simplilearn!
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Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.
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6.Methylation
7.Sulfate conjugation
8.Nucleoside and Nucleotide formation
9.Amino Acid Conjugation
10.Fatty Acid and Cholesterol Conjugation
Drug design is the process of creating new drugs by using knowledge of a biological target.
Drug design considers metabolism to optimize pharmaco kinects ( ADME: Absorption , Distribution , Metabolism , Excretion )
Cytochrome CYP450 enzyme in Drug Metabolism is vital in drug design to enhance efficacy , reduce toxicity and improve bioavailability.
Cytochrome P450 enzymes (CYPs) are a superfamily of heme -containing proteins found from bacteria to human.
Cytochrome P-450 activity in various organs like Liver,Lung ,Kidney , Intestine,Placenta
Adrenal and Skin and they shows the relative activity with repect to their organs in the process of drug metabolism.
Most important CYP450 enzymes are CYP1A2 , CYP2C9 , CYP2E1
,etc...
Toxic Effects of Drug Metabolism
Toxicity: Accumulation of Excess of medications in the Blood Stream.
Ariens (1948) and Mitchell and Horning (1984) deal with this topic.
Some examples of Metabolism-Linked Toxicity are
1.Acetaminophen (paracetmol)
2.Isoniazid ( TB drug)
3.Chloroform
4.Dapsone
5.Diazepam
6.Salicylate
7.Halothane (Anesthetic)
8.Tamoxifen (Breast Cancer drug )
9.Clozapine(Antipsychotic)
These drugs are differentiates with the TOXIC METABOLITE , TOXICITY OF METABOLITE.
References for this topic also mentioned at the end.
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- 2. What is RAG ? Retrieval-Augmented Generation Retrieval-Augmented Generation (RAG) is a technique that enhances the performance of language models by combining them with a retrieval system. External Information Accurate
- 3. How RAG works ? STEP 1 : Understanding the question with LM STEP 3 : Takes the information combines with already known information STEP 2 : Searching for best information from outside sources. STEP 4 : Complete and accurate output.
- 4. What is Fine tuning? Fine tuning is a process used in machine learning particularly with large model to adapt a pre-trained model to perform a specific task more effectively. General Knowledge Improvement
- 5. How Fine-Tuning Works? STEP 1 : Start with a pre- trained model. STEP 3 : Fine tuning process. STEP 4 : Evaluate the model. STEP 2 : Prepare a specific dataset.
- 6. What is Prompt tuning? Prompt tuning is a technique used to optimize the way prompts are presented to a language model in-order to steer its responses towards a desired outcome. General Knowledge Improvement
- 7. How Prompt tuning Works? STEP 1 : Define Objectives STEP 3 : Evaluate Performance STEP 2 : Design Prompts STEP 4 : Refine and Iterate
- 8. Major Differences RAG combines a language model with a retrieval system. Approach Prompt tuning modifies the prompts to guide the model’s response Fine-Tuning further trains a pre-trained language model on a specific dataset.
- 9. Major Differences How Airplanes Fly?
- 10. Major Differences RAG improves accuracy and relevance Purpose Prompt tuning directs the model to generate more relevant responses. Fine-Tuning customizes the model to perform better.
- 11. Major Differences How a car Engine works?
- 12. Major Differences RAG answers questions with the latest information Use - Case Prompt tuning customizes output style. Fine-Tuning creates a chatbot tailored for customer support
- 13. Major Differences RAG relies on external databases or documents Data dependency Prompt tuning operates on the pre-trained model's existing knowledge base. Fine-Tuning depends on a fixed, task-specific dataset during the training phase
- 14. Similarity RAG Fine- tuning Prompt- tuning improve the relevance and accuracy of the model's responses