Skip to main content

GenAI Productivity: Ideas to project proposal 2


In a previous post Ideas to project proposal 1 I discussed a relatively simple way to get GenAI to create a few ideas for a specific project and then create the start of a project proposal. 

Now we are going to extend the idea and use Chain of Density Prompt to take the proposal and make it even richer. Chain of Density approach was discussed also in an earlier post Improving your summarising

A proposal is feed in and then an adapted forrm of the Chain of Density is used to refine the detail. The primary idea is that a richer in terms of information is produced.

Prompt
using the loaded file generate increasingly concise entity-dense summarises of the project proposal (now call it Article) . Sections are TITLE; INTRODUCTION ; STATEMENT OF THE PROBLEM ; PURPOSE OF THE STUDY ; ASSUMPTIONS AND HYPOTHESIS; DATA COLLECTION PROCEDURES; DATA ANALYSIS; DATA VALIDATION; ETHICAL ISSUES; REFERENCE LIST. 

 For each section: 
 Repeat 5 times, the following steps specified below as Step 1 and Step 2, 
Step 1: Identity 1-3 informative Entities (“;” delimited)from the article which are missing from the previous generated summary. 
Step 2: Write a new denser summary of identical length which covers every entity and detail from the previous summary plus the Missing Entities. 

 A Missing Entity is:- 
-Relevant: to the section. 
-Specific: descriptive yet concise (5 words or fewer). 
-Faithful: present in the section. 
-Anywhere: located anywhere in the section 

 Guidelines: 
-Make every word count: rewrite the previous summary to improve flow and make space for additional entities. 
-Make space with fusion, compression, and removal of uninformative phrases like “the article discusses”. 
-The summaries should become highly dense and yet self-contained, e.g. easily understood without the Article. 
-Missing entities can appear anywhere in the new section's summary. 
-Never drop entities from the previous summary. If space cannot be made, add fewer new entities. 

 Remember , use exact same number of words for each section summary specified next to Section name (e.g. TITLE (50))


You might need to repeat the process a few times and remember GenAI is not really understanding what it is writing - it still needs the human in the loop.


All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with. Twitter @scottturneruon

Comments

Post a Comment

Popular posts from this blog

Using an LLM to Find Themes using Thematic Analysis in an Academic Career

B rief recap In an earlier post, “Same prompt, four AIs: why answers aren’t always the same” , I looked at what happened when the same prompt was given to four different LLMs. Unsurprisingly, perhaps, the answers were not identical. That raised an interesting follow-on question: what was the prompt actually trying to do? The answer is that it was trying to support a form of thematic analysis. In that case, the object of analysis was an academic profile, including my Google Scholar profile. The version I discuss here is a modified prompt, used with Claude.ai, where I uploaded a CV and asked the system to find other appropriate public resources connected with a named person and institution. The aim was not simply to summarise the CV, but to identify visible and less visible themes across a whole career. Why use thematic analysis? Thematic analysis is widely used by academics, especially in qualitative research. Braun and Clarke’s well-known paper, Using thematic analysis in psy...
Post a Comment
Read more

Prompt Engineering, Context Engineering and Agentic AI in Higher Education: A Short Briefing

AI in Education Prompt engineering, context engineering and agentic AI are often used interchangeably, but the literature treats them as distinct. Prompt engineering concerns crafting effective single instructions to a model (Glean, 2026). Context engineering is the broader discipline of designing and managing the entire informational environment around a model—memory, retrieval, tool outputs and conversation state—rather than a one-off instruction (Abstracta, 2026); prompt engineering operates within the context window, while context engineering determines what fills it (arXiv:2606.12422, 2026). Agentic AI describes systems that plan and execute multi-step tasks with delegated autonomy, raising organisational questions of accountability rather than purely technical ones (MIT Sloan, 2026; Palo Alto Networks, 2026). There is genuine debate about prompt engineering's durability. IEEE Spectrum (2025) reported research suggesting prompting is increasingly performed by models themse...
Post a Comment
Read more

AI as a Mirror: Transforming Vague Student Ideas into a More Rigorous Project Agreement

The Problem: The "Generic App" and the "Time Sink" We’ve all been there: a student walks into a 1-to-1 with a vague desire to "do something with AI" or "build a fitness app." You spend 45 minutes trying to find a technical "hook" that justifies a Level 6 or Level 7 grade, only for the student to drift back into "CRUD app" territory by week three. The Philosophy: AI as a Mirror Instead of you doing the heavy lifting, this workflow uses AI as a Mirror . It reflects the student’s own skills and career goals back to them, but with the structural rigour of a virtual supervisory team. It’s not about the AI "giving" the idea; it’s about the AI forcing the student to defend and refine their own concepts until they hold water. The Framework: 3 Months of Rigour This prompt is specifically designed for intensive/conversion MSc or summer capstone projects . It assumes a tight 12-week implementation window. By forcing the AI to w...
Post a Comment
Read more