LN: Shum (2025) — Pensar con Prompts
Bibliographic Reference
Citation: Shum, J. (2025). Pensar con prompts: La guía definitiva de la ingeniería de prompts. Independently published. Note: Independently published in 2025; available via major online book retailers. No official author website confirmed at time of writing. Verify current availability via retailer search.
Metadata Note
This book is self-published. Author details and edition information reflect vault metadata. The Spanish-language original title is preserved; the summary below is in English per PUMA convention.
Pass 1 — Bird’s Eye View (5 Cs)
| C | Assessment |
|---|---|
| Category | Practical guide + methodology compendium |
| Context | Spanish-language comprehensive reference for prompt engineering techniques; fills a gap in accessible Spanish-language AI resources |
| Correctness | Synthesizes published techniques (CO-STAR, CoT, Self-Consistency, ReAct, etc.) with practical examples |
| Contributions | (1) CO-STAR framework formalization in Spanish; (2) Curated taxonomy of 20+ prompting techniques; (3) Applied examples for business and research contexts |
| Clarity | Excellent. Structured for both beginners and practitioners. |
Relevance: ⭐⭐⭐⭐
The CO-STAR framework in this book is the primary structural guide for PUMA’s prompt design. The taxonomy of techniques maps directly to PUMA’s experimental prompt strategy matrix.
Pass 2 — Content
CO-STAR Framework (Detailed)
Shum operationalizes CO-STAR as a six-section prompt template:
[C] CONTEXTO: Quién eres y cuál es el trasfondo de la tarea
[O] OBJETIVO: Qué quieres que el modelo haga exactamente
[S] ESTILO: Cómo debe escribir o responder
[T] TONO: El registro emocional o actitudinal de la respuesta
[A] AUDIENCIA: A quién va dirigida la respuesta
[R] RESPUESTA: El formato exacto que esperas
Key principle: The [R] section (Response format) is the most critical for programmatic use. Specifying JSON schema in [R] reduces format errors by 60–80% compared to unstructured prompts.
Prompting Technique Taxonomy
| Technique | Category | When to Use | PUMA Status |
|---|---|---|---|
| Zero-Shot | Basic | Simple, well-defined tasks | H1/H2 baseline |
| Few-Shot | Basic | Pattern matching, classification | H1/H2 primary |
| Zero-Shot CoT | Reasoning | Multi-step inference | H1 augmented |
| Few-Shot CoT | Reasoning | Complex estimation with examples | H2 primary |
| Self-Consistency | Reliability | High-stakes, ambiguous cases | H2 production |
| ReAct | Agentic | Tool use + reasoning | Stage 4+ |
| COSTAR | Structure | Any LLM task | All PUMA prompts |
| Role Prompting | Persona | Specialized expert behavior | PM agent persona |
| Meta-Prompting | Advanced | Self-improving prompts | Research extension |
| Tree of Thoughts | Advanced | Complex multi-path decisions | Future work |
Structured Output Best Practices
- Always include a concrete JSON example in the prompt — not just a schema description
- Specify field constraints explicitly:
"priority": "one of [Critical, High, Medium, Low]" - Include a “rationale” field even if not consumed — forces model to reason before classifying
- Add an escape hatch:
"confidence": 0–1; if confidence < 0.6, set type to 'uncertain'
Prompt Anti-Patterns (to avoid in PUMA)
- Vague instructions: “Analyze this issue” → “Classify this issue as Bug, Feature, Task, Improvement, or Sub-task”
- Missing format specification: No JSON schema → inconsistent outputs, parse failures
- Role without persona: “You are an AI assistant” → “You are a senior project manager with 10 years of Agile experience”
- No examples for classification: Zero-shot on subjective tasks → high variance outputs
- Overlong prompts: >4K tokens for simple classification → model loses focus on task
Human-AI Co-Creation and Generative Cognition
Beyond the technical taxonomy, Pensar con Prompts advances a broader thesis about what prompting represents epistemologically: it is a form of generative cognition — thinking through and with an AI system rather than simply instructing it.
Key claims:
- Prompts are not commands to a tool; they are the articulation of thought in a form that another mind (the model) can extend
- The act of writing a good prompt requires the human to clarify their own thinking: vague prompts reflect vague intentions
- The output of a well-designed prompt session is not just the AI’s response — it is the human’s clarified understanding plus the AI’s contribution
This framing positions prompt engineering as a cognitive practice, not merely a technical skill. The quality of human-AI interaction depends as much on the human’s ability to think clearly as on the model’s capability.
Implications for Human-AI Collaboration
Shum argues that the most valuable prompt engineers are not those who memorise technique lists — they are those who develop a genuine understanding of how language models “think” (statistically, contextually, pattern-matching on training distributions). This understanding enables:
- Anticipating model failure modes before they occur
- Diagnosing why a prompt produces poor results
- Iterating deliberately rather than randomly
- Designing prompts that surface the model’s genuine capabilities rather than averaging over its patterns
Generative AI and Cognitive Extension
The book closes with a philosophical claim: LLMs are the first general-purpose cognitive extension tools available to everyone. Unlike calculators (which extend numerical cognition) or search engines (which extend memory), LLMs extend the full range of linguistic-reasoning cognition. The implications are systemic: how humans think, write, and decide will change fundamentally in a world where generative AI is ubiquitous.
PUMA Integration
- Prompt design: CO-STAR structure is the template for all PUMA H1/H2 prompts
- Technique selection: Shum’s taxonomy provides vocabulary for PUMA’s experimental conditions
- Anti-pattern checklist: Review all PUMA prompts against Shum’s anti-pattern list before experiment execution
- Spanish-language resource: Relevant for PUMA academic context (Spanish university TFG)
- Generative cognition framing: Supports PUMA’s claim that prompt engineering is a methodology, not just a parameter
Related Notes
- PN-COSTAR-SelfConsistency — permanent note on these techniques
- EX-Hypotheses-H1-H2 — where these techniques are applied
- PN-CoT-FewShot-Prompting — CoT and few-shot prompting techniques
- PN-ActiveReading-CognitivePractice — cognitive offloading; prompt engineering as cognitive practice
- PN-RCOIF-Framework — RCOIF as a complementary Spanish-language prompting framework