Apa wae batasan LLM lan carane sampeyan ngatasi?

Question

Accepted Answer

LLM iku kuat nanging duwe **batasan** sing signifikan — hallucination, knowledge cutoffs, kekurangan penalaran sejati, non-determinism, lan liya-liyane. Ngerti batasan-batasan iki lan carane ngabungake iku essential kanggo nggunakake LLM kanthi tanggung jawab lan efektif.

## Batasan kunci

```text
✗ HALLUCINATION → generate plausible-sounding but FALSE information (confidently wrong) →
  the most critical limitation (they predict likely text, not verified truth)
✗ KNOWLEDGE CUTOFF → only know up to their training date → no current info
✗ NO TRUE UNDERSTANDING/REASONING → pattern-match text; can fail at logic, math, novel
  reasoning; no real comprehension
✗ NON-DETERMINISTIC → same prompt → different outputs (variability)
✗ NO REAL-TIME/external knowledge by default; limited CONTEXT window (can't hold unlimited info)
✗ Can be BIASED; can be manipulated (prompt injection); inconsistent
```

## Mitigating the limitations

```text
✓ HALLUCINATION → use RAG (ground in retrieved facts); verify outputs; cite sources;
  don't trust blindly for facts; human review for important decisions
✓ KNOWLEDGE CUTOFF → RAG/tools for current info; provide context in the prompt
✓ REASONING limits → chain-of-thought prompting; break down tasks; use tools (calculators,
  code) for math/logic; verify
✓ NON-DETERMINISM → design for variability; structured output + validation; lower temperature
✓ Validate/sanitize outputs; guardrails; human-in-the-loop for critical uses
```

## Apa iki penting

Ngerti batasan LLM lan carane ngatasi iku pengetahuan level senior sing berharga amarga **nggunakake LLM kanthi tanggung jawab lan efektif butuh ngerti batasan-batasane**, mula essential kanggo mbangun aplikasi AI sing reliable.

LLM iku kuat nanging duwe cacat, lan ngerti batasan-batasane iku kritis kanggo nggunakake kanthi bener.

Ngerti **batasan kunci** — khususnya **hallucination** (ngasilake informasi kang kayane masuk akal nanging palsu kanthi yakin, batasan paling kritis amarga LLM nang-predict teks kang likely tinimbang kasunyatan sing terverifikasi), **knowledge cutoffs** (mung ngerti nganti tanggal training), **ora ana ngerti utawa penalaran sejati** (pattern-matching teks, gagal ing logika lan matematika, kurang komprehension sejati), **non-determinism** (prompt sing sama menehi output beda), context windows terbatas, lan kerentanan marang bias lan manipulasi — iku essential awareness kanggo sapa bae kang nggunakake LLM, amarga ora ngerti iki dadi misuse (percaya output sing salah, ngarep-arep capabilities kang LLM duwe).

Ngerti carane **mitigating the limitations** iku pengetahuan praktikal kunci: ngatasi hallucination via **RAG** (grounding ing retrieved facts), verification, citations, lan ora membabi buta percaya LLM kanggo facts (kanthi human review kanggo keputusan penting); ngatasi knowledge cutoffs via RAG lan tools; ngatasi reasoning limits via chain-of-thought prompting, task breakdown, lan nggunakake tools (calculators, code) kanggo matematika/logika; ngatasi non-determinism via structured output lan validation; lan nggunakake guardrails lan human-in-the-loop kanggo uses kang kritis.

Mitigasi-mitigasi iki essential kanggo mbangun LLM applications kang reliable kang bisa berjalan sanajan LLM duwe cacat.

Insight kritis iku LLM kudu digunakake **kanthi awareness marang batasan-batasane** (khususnya hallucination) lan mitigations kang sesuai — membabi buta percaya output LLM iku dangerous, sadurunge ngerti lan mitigating limitations ngaktifake penggunaan kang efektif, tanggung jawab.

Amarga nggunakake LLM kanthi tanggung jawab lan efektif butuh ngerti batasan-batasane kang signifikan (khususnya hallucination) lan carane ngatasi (RAG, verification, tools, human review), lan amarga iki essential kanggo mbangun AI applications kang reliable, ngerti batasan LLM lan carane ngatasi iku pengetahuan level senior AI kang berharga, essential — kritis kanggo nggunakake LLM kanthi tanggung jawab lan efektif (ngerti hallucination lan batasan liyane, lan mitigating via RAG, verification, tools, lan human oversight), essential kanggo mbangun AI applications kang reliable, lan reflecting mature understanding manawa LLM iku tools kang kuat nanging duwe cacat kang butuh careful, informed use.