Machine translation output in 2026 is good. It’s not good enough.

Raw MT from DeepL, Google, or any major engine produces text that’s usually comprehensible but rarely polished. The translations are technically correct but feel off: wrong register, inconsistent terminology, awkward phrasing that no native speaker would use.

Most organizations address this through human post-editing. Translators review MT output, fix problems, and produce final text. This works, but it’s essentially using humans to compensate for systematic MT limitations—the same limitations, segment after segment, project after project.

A better approach: systematic enhancement before human review.

The enhancement opportunity

MT output has predictable weaknesses:

Terminology inconsistency. The same source term gets translated different ways in different segments. “Dashboard” becomes “Tableau de bord” here and “Panneau de contrôle” there.

Register mismatch. MT tends toward a generic, neutral register. Content that should be formal comes out conversational. Content that should be friendly comes out stiff.

Structural awkwardness. Sentence structure that makes sense in the source language gets translated literally, producing grammatically correct but unnatural target text.

Missing context. MT translates segments in isolation. Pronouns, references, and terminology choices that should reflect surrounding context don’t.

Each of these problems is individually small. Collectively, they’re the difference between “technically translated” and “actually good.”

The enhancement pipeline

Rather than asking post-editors to fix every problem manually, a systematic pipeline addresses them automatically:

Stage 1: MT generation. Run content through a strong MT engine—DeepL, Google, Azure—to get the baseline translation. This handles the heavy lifting of producing comprehensible target language.

Stage 2: Terminology application. Cross-reference the translation against approved terminology. Where MT used a non-standard term, replace it with the correct one. This enforces consistency automatically.

Stage 3: Style adjustment. Apply style guide rules to correct register issues. If the content should use formal address, ensure that. If certain phrasings are preferred, substitute them.

Stage 4: AI refinement. Pass the corrected translation through an LLM with instructions to improve fluency while preserving meaning. The LLM sees the context MT didn’t, smoothing awkward structures and improving flow.

Stage 5: Quality scoring. Evaluate the enhanced translation against quality criteria. Flag segments that still need attention; pass acceptable segments to human review.

Each stage addresses specific weaknesses. The cumulative effect is output significantly better than raw MT.

Terminology automation in practice

Terminology enforcement is the most straightforward enhancement step and often the highest impact.

A glossary contains approved translations for key terms. During enhancement:

1. Identify source terms that appear in the glossary
2. Find those terms’ translations in the MT output
3. If MT used a different translation, substitute the approved term
4. Handle linguistic adaptation (gender agreement, declension, etc.)

The result: every instance of “Tableau de bord” where it should be “Dashboard” gets corrected. Every product name, technical term, and branded phrase uses the approved translation.

Human post-editors no longer need to remember and enforce terminology manually. They can focus on issues that require judgment rather than mechanical consistency checks.

Style guide automation

Style guides define preferences that MT ignores: formality level, banned phrases, preferred constructions, punctuation conventions.

Style enhancement applies these rules systematically:

• Convert tu/vous forms based on audience requirements
• Replace deprecated terms with current alternatives
• Adjust number formatting for target locale
• Apply capitalization conventions
• Standardize punctuation spacing

Rules engine approaches work for straightforward substitutions. More complex style requirements—“prefer active voice” or “use shorter sentences for this content type”—may need LLM-based application.

AI refinement: the contextual layer

The final enhancement stage uses an LLM not to translate, but to improve existing translations.

The prompt provides:

• The source text
• The enhanced MT output
• Surrounding segments for context
• Style instructions

The instruction: “Improve the fluency and naturalness of this translation while preserving the meaning exactly.”

This step catches issues that rule-based approaches miss:

• Awkward literal translations that are technically correct but unnatural
• Pronoun ambiguity from missing context
• Sentence flow issues across segments
• Subtle register inconsistencies

The LLM sees the forest, not just the trees. It can make adjustments that require understanding the broader text.

When enhancement isn’t enough

Enhancement improves average quality but doesn’t guarantee perfection. Some segments need human judgment regardless:

• Ambiguous source content where meaning isn’t clear
• Creative content where literal accuracy matters less than impact
• Culturally sensitive content requiring local expertise
• Complex technical content in specialized domains

Enhancement reduces the volume of segments needing attention, but human review remains essential for final quality.

Measuring enhancement impact

Enhancement effectiveness is measurable:

Post-editing time. Enhanced MT should require less editing than raw MT. Track actual time to see the improvement.

Edit distance. How much does human post-editing change the enhanced output? Lower edit distance indicates better enhancement.

Quality scores. Apply consistent quality evaluation to raw MT, enhanced MT, and final output. Enhancement should close the gap toward final quality.

Issue categorization. Track what types of issues post-editors fix. If the same issues appear repeatedly, they’re candidates for enhancement rules.

This data enables continuous improvement: identify what enhancement misses, add rules or improve prompts, measure again.

The competitive advantage

Many organizations use MT. Few systematically enhance it before human review.

The result: they either accept lower quality (raw MT with light touch-up) or pay for heavier editing (humans fixing everything MT got wrong). Neither is optimal.

A systematic enhancement pipeline produces better output at lower post-editing cost. The investment in building and tuning the pipeline pays dividends across all content processed through it.

For organizations with significant translation volume, this isn’t a marginal improvement—it’s a structural advantage.

Language Ops provides configurable post-MT enhancement with terminology automation, style guide application, and LLM refinement. See the quality difference on your own content.