Reliable Document Summarisation Pipelines
Build summarisation that handles long documents, keeps facts straight and can be checked against the source.
A taste of a lesson
Our meeting summaries sometimes say a proposal was approved when it was actually rejected. Why?
That is a lost negation or a confused outcome, and it is common when a discussion goes back and forth. Two fixes help. Structure the summary with a decisions section where each decision has a status and a timestamp reference to the transcript. Then add a check: for each decision, a separate call reads the referenced passage and confirms the status, flagging mismatches for review. Also chunk on speaker turns so outcomes are not split. How long are your transcripts, and do they have timestamps?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Define summary purpose, audience, length and must include items
- Choose single pass, map and combine or iterative pipelines for long documents
- Produce structured summaries with source references
- Check summaries for omissions, altered figures and unsupported claims
- Evaluate summaries with a rubric on a reviewed sample
Lesson plan
- 1 What a good summary is for Define purpose, audience and must include items before prompting. Start
- 2 Structured summaries Design summaries with sections that are easy to check and use. Start
- 3 Long document pipelines Compare map and combine, iterative and hierarchical approaches. Start
- 4 How summaries go wrong Recognise the typical faithfulness failures. Start
- 5 Faithfulness checks Automatically flag summary claims not supported by the source. Start
- 6 Evaluating summaries Score summaries with a rubric and track quality over time. Start
Try asking
About this tutor
For developers building features that summarise reports, meeting transcripts, contracts, research papers or support threads at scale. You learn to define the purpose and audience of a summary, choose between single pass, map and combine, and iterative approaches for long documents, and design structured summaries such as decisions, actions and risks. The tutor focuses on the failures that matter: omitted key points, invented or altered numbers and dates, lost negations and misattributed statements. You build checks that compare claims with the source, add section references, and evaluate summaries with a rubric rather than a gut feeling.
Reviews
4.3
3 ratingsSample
- Wei Z.Sample
Adding timestamp references to each decision made our summaries checkable. The number check against the source caught several altered figures in week one.
- Hamza I.Sample
Strong on faithfulness. Would have liked more on summarising tables, but the advice to quote figures directly was useful.
- Ellen M.Sample
Clear comparison of map and combine versus iterative. The rubric for evaluation helped us stop arguing about which summary felt better.
About the teacher
Structured output, tool calling and safe input handling for LLM applications that must behave predictably
9 tutors 308 lessons taught Sample
I teach the parts of LLM apps where free text has to meet real software: JSON that must parse, tools the model calls, images and documents coming in, and users who send things you did not plan for. I spent years writing integrations between messy systems, which taught me to treat every input as untrusted and every output as something...
See Greta's profile and tutorsMore like this
Other tutors on the same or nearby topics.