Section 1: Chain Explanation

In the upcoming sections, I break down the structure of AI/ML tasks into a logical chain of components. This includes defining the task clearly, reviewing established benchmarks, outlining how models are evaluated using meaningful metrics, exploring both machine learning-based and traditional optimization strategies, and finally examining model architectures and system design patterns. This chain-based breakdown not only helps structure technical understanding but also provides a template to generate prompts for analyzing or designing intelligent systems.

You can replace [X] with the topic of interest to you so that you can use that as a prompt template for doing a comprehensive research in that domain/task.

Section 2: Formulating the Chain as a Prompt

2.1 Task Definition

• Provide a clear, technical definition of [X]
• Explain the core problem this task addresses
• Identify key applications and use cases
• Describe any notable subtasks or variants

2.2 Benchmarks

• Identify the primary datasets used to evaluate [X] systems
• List the most recent benchmarks (2023-2025), including publication dates and popularity metrics
• Describe any benchmark evolution or trends in the field
• Note any benchmark limitations or controversies

2.3 Evaluation Aspects and Metrics

• Enumerate the key dimensions on which [X] systems are evaluated
• For each evaluation aspect, detail:
  • Specific metrics used (with mathematical definitions when applicable)
  • What these metrics measure and why they matter
  • Any known limitations or biases in these metrics
• Describe how holistic evaluation is performed (if applicable)

2.4 Optimization Approaches

A. ML-Based Optimization

• Detail the primary loss functions used for [X]
• Explain key training and fine-tuning strategies
• Describe prompt engineering or optimization techniques specific to [X]
• Outline performance improvement methods (e.g., RL, RLHF)

B. Traditional Optimization

• Identify non-ML algorithmic approaches that enhance [X] systems
• Explain efficiency patterns or software design patterns applied
• Detail any hybrid approaches combining traditional and ML techniques
• Describe system-level optimizations for production deployment

2.5 Model and Architecture

A. ML Models

• List the state-of-the-art model architectures for [X]
• Describe model sizes, parameters, and computational requirements
• Explain key architectural innovations specific to [X]
• Identify trends in model development for this task

B. Traditional Software Components

• Detail supplementary non-ML components in [X] systems
• Explain the role of symbolic/rule-based systems (if applicable)
• Describe integration patterns between ML and non-ML components
• Outline infrastructure considerations for production systems

For each section, cite recent research (2023–2025 where available) and industry practices. Highlight tensions between competing approaches and note open research questions in the field.

Section 3: Examples

Here we cover two examples that follow the structure we covered in the previous section - one example is manually conducted and the other is automatically conducted by using the structure of the previous section as a prompt and getting an LLM to do the research :)

3.1 Automatic Text Summarization (Case Study 1)

Ok. Let's imagine that our TASK is "Automatic Text Summarization using Seq2Seq Approach". To conduct serious research on this, we can’t just jump into model training or coding. We first need to carefully investigate each and every one of the five parts of the pipeline I’ve described earlier — from defining the task, to evaluating it, to optimizing and designing the architecture. Once that research is complete, we then organize our findings into a structured proposal to submit for academic, industry, or personal R&D purposes.

Since my Master’s in Computer Science focused on this exact topic, I can quickly sketch out a brief study that demonstrates what this process looks like in action.

So, the task is Automatic Text Summarization. That means taking a long piece of text (like a news article or scientific paper) and generating a shorter version that captures the most important points. It’s a classic sequence-to-sequence problem: given an input sequence (the document), the model needs to generate an output sequence (the summary).

There are a few well-known benchmark datasets for this task. Two of the most popular are XSum and CNN/DailyMail (CNN/DM). These datasets give us a standard way to train and evaluate models on summarization.

Now, when it comes to evaluation, summarization is tricky because it's subjective — there’s no single "correct" summary. So we break down the evaluation into different aspects:

• Grammatical correctness – does the summary read well?
• Brevity – is the summary concise?
• Informativeness and recall – does it cover the important points from the original text?
• Faithfulness – does the summary avoid hallucination (i.e., adding facts not in the source)?

Each of these evaluation goals maps to different metrics. For example:

• To evaluate hallucination, we might extract factual triples (subject, relation, object) from the summary and the source text, then check how many of them are actually supported by the original.
• To evaluate recall of key information, we might use metrics like ROUGE, which compares n-gram overlap between the generated and reference summaries.

Since we’re framing our task as “Automatic Text Summarization using Deep Learning Approaches”, our models typically rely on gradient descent optimization and are trained using a Maximum Likelihood Estimation (MLE) loss. This means the model is learning to maximize the probability of generating the correct summary token by token.

Some of the most popular model architectures in this space include BERT (used for extractive approaches), Longformer (for long documents), and encoder-decoder models like BART or T5 that are commonly used in abstractive summarization settings.

This kind of structured analysis — clearly defining the task, choosing the right benchmarks, carefully evaluating with relevant metrics, and selecting appropriate optimization techniques — is what helps us build solid research foundations or practical applications in summarization.

3.2 AI Agents (Case Study 2)

Using a Large Language Model, I have conducted a research on AI Agent topic. The result is shared by the following PDF: