Pith Number

pith:YPFYFNW2

pith:2024:YPFYFNW2KJ4VVOYUZOV2YKXV46

not attested not anchored not stored refs resolved

The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

Aayush Gupta, Alexander Hoyle, Amanda Liu, Ashay Srivastava, Chau Pham, Chenglei Si, Dayeon Ki, Denis Peskoff, Feileen Li, Gerson Kroiz, Giuseppe Sarli, Hevander Da Costa, Hudson Tao, HyoJung Han, Igor Galynker, Inna Goncearenco, Jules White, Konstantine Kahadze, Marine Carpuat, Megan L. Rogers, Michael Ilie, Nishant Balepur, Philip Resnik, Pranav Sandeep Dulepet, Saloni Gupta, Sander Schulhoff, Saurav Vidyadhara, Sevien Schulhoff, Shyamal Anadkat, Sweta Agrawal, Yinheng Li

A survey organizes prompt engineering into a taxonomy of 58 LLM techniques and 40 for other modalities, supported by a 33-term vocabulary and best practices.

arxiv:2406.06608 v6 · 2024-06-06 · cs.CL · cs.AI

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

this paper presents the most comprehensive survey on prompt engineering to date

C2weakest assumption

The literature search and categorization process captured a representative and exhaustive set of prompting techniques without significant omissions or misclassifications.

C3one line summary

This systematic survey organizes prompt engineering into a taxonomy of 58 LLM techniques and 40 others, supplies a shared vocabulary, and offers guidelines for state-of-the-art models.

References

27 extracted · 27 resolved · 5 Pith anchors

[1] Agrawal, P., Craig, N., Madden, A., and Lombera, I 2023

[2] Muhammad Awais, Muzammal Naseer, Salman Khan, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, and Fahad Shah- baz Khan 2023

[3] In Proceedings of the 17th Conference of the European Chapter of the As- sociation for Computational Linguistics, pages 2455– 2467, Dubrovnik, Croatia 2023

[4] arXiv preprint arXiv:2405.00200 , year= 2022

[5] Sparks of Artificial General Intelligence: Early experiments with GPT-4 2023 · arXiv:2303.12712

Formal links

2 machine-checked theorem links

Cited by

36 papers in Pith

PromptCOS: Towards Content-only System Prompt Copyright Auditing for LLMs

PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models

PRIMETIME : Limits of LLMs in Temporal Primitives

AtelierEval: Agentic Evaluation of Humans & LLMs as Text-to-Image Prompters

From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents

Receipt and verification

First computed	2026-05-17T23:38:53.842061Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c3cb82b6da52795abb14cbabac2af5e78660026cacc8be6788ea5e289a054fce

Aliases

arxiv: 2406.06608 · arxiv_version: 2406.06608v6 · doi: 10.48550/arxiv.2406.06608 · pith_short_12: YPFYFNW2KJ4V · pith_short_16: YPFYFNW2KJ4VVOYU · pith_short_8: YPFYFNW2

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YPFYFNW2KJ4VVOYUZOV2YKXV46 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: c3cb82b6da52795abb14cbabac2af5e78660026cacc8be6788ea5e289a054fce

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c0ecf27a8df7f9ccb02f872995985b77c58a656ff4792ab3ec2123fa4b931360",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2024-06-06T18:10:11Z",
    "title_canon_sha256": "9bfdc0b855be5f2c43b2f8034696222ac18f87815ec861f29d5160033cc0f72b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2406.06608",
    "kind": "arxiv",
    "version": 6
  }
}